Projects & Theses

Below you find a collection of BSc and MSc projects and theses that can be carried out within the MaP community.

Advanced Robotic In-Situ Tissue Repair

The future of advanced tissue repair lies in precise, minimally invasive techniques that enhance patient outcomes while reducing recovery time. This innovative project aims to revolutionize in-situ tissue repair by designing and implementing a cutting-edge robotic tool, purpose-built for the medical frontier. Combining state-of-the-art robotics, real-time feedback systems, and versatile functionalities, this research will address the complex challenges of advanced tissue repair with a focus on precision and patient safety.

Keywords

Embedded Systems, PCB Design, Robotics, Biomechatronics, CAD, Medical Device Design, Biomedical Engineering, Electrical Engineering, Mechanical Engineering, Python, ROS, Surgery, Biomaterials, Biomedical Device Innovation

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Semester Project , Bachelor Thesis , Master Thesis , ETH Zurich (ETHZ)

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Published since: 2025-04-10 , Earliest start: 2025-05-01

Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne , Empa , University of Zurich

Organization Nanoparticle Systems Engineering Laboratory

Hosts Balta Nisa

Topics Information, Computing and Communication Sciences , Engineering and Technology

Master Thesis: Developing Functional Inclusion Body Tags

Inclusion bodies are dense protein aggregates formed in the cytoplasm of bacteria like E. coli. While often regarded as problematic, proteins that are sequestered in such inclusion bodies continue to be active and are simple to purify. By tagging proteins of interest with specific “inclusion body tags”, even proteins that would remain soluble can be sequestered into inclusion bodies upon expression. This project aims to design and evaluate novel inclusion body tags with tailored properties for diverse applications. Of special interest are tags that are detectable in electrospray ionization mass spectrometry (ESI-MS).

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Master Thesis

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Published since: 2025-04-10 , Earliest start: 2025-05-01 , Latest end: 2026-01-31

Organization Bioprocess Laboratory

Hosts Kocar Berk

Topics Chemistry , Biology

Graph Neural Networks for Structural Engineering: Bridging MPNNs and FEM

Structural design and analysis operations for graph-like discrete systems, such as trusses and frames, typically involve solving systems of equations that enforce equilibrium, compatibility, and force-displacement relationships. Advances in Message-Passing Neural Networks (MPNNs) have demonstrated strong performance across fields ranging from molecular modelling to physics-based simulations. Compellingly, MPNNs naturally operate on graph-structured data, and recent work suggests a mathematical equivalence between MPNNs and classical structural operations. Graph-based methods offer scalability and unlock deep learning applications for structural analysis. However, a unified and effective integration of these approaches has yet to be convincingly demonstrated. This thesis aims to formalise this relationship by developing an MPNN-based framework for forward and inverse structural analysis. The research will investigate the use of MPNNs formulations for structural analysis operations based on Finite Element Analysis (FEM). The proposed research will contribute to the understanding of the underlying mechanisms of MPNNs and help develop more efficient and interpretable MPNNs as alternatives to structural simulators.

Keywords

Message-Passing, Graph Neural Networks, Structural Analysis

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Master Thesis

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Published since: 2025-04-09

Organization ETH Competence Center - ETH AI Center

Hosts Duthé Gregory , Duthé Gregory

Topics Information, Computing and Communication Sciences , Engineering and Technology

Smart Microcapsules for Biomedical Advances

This Master's thesis/semester project focuses on the microfluidic fabrication of microcapsules with multi-environmental responsiveness. The aim is to develop microcapsule-based microrobots capable of adapting to various environmental cues. We envision that these microrobots will be used for complex tasks in biomedical applications.

Keywords

Microfluidics, Microcapsules, Microrobotics, Responsive Polymers, Biomedical Engineering

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Semester Project , Internship , Master Thesis , Student Assistant / HiWi , ETH Zurich (ETHZ)

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Published since: 2025-04-08 , Earliest start: 2025-07-01

Organization Multiscale Robotics Lab

Hosts Hu Minghan

Topics Medical and Health Sciences , Engineering and Technology , Chemistry

Low-Voltage Soft Actuators for Developing Untethered Robotic Systems

We are building the next generation of HALVE (Hydraulically Amplified Low-Voltage Electrostatic) actuators which are flexible, pouch-based electrostatic actuators that operate at voltages 5–10× lower than traditional soft electrostatic systems. You will help us explore novel actuator geometries, ultra-thin functional layers, and new fabrication techniques to unlock scalable, energy-efficient soft robotic systems.

Keywords

soft robotics, low-voltage actuation, dielectric elastomers, electrostatic actuators, fabrication, PVDF-TrFE-CTFE, vapor deposition, CNC sealing, mechatronics, materials science

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Semester Project , Bachelor Thesis , Master Thesis

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Published since: 2025-04-07 , Earliest start: 2025-04-14 , Latest end: 2026-01-31

Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne , Empa

Organization Soft Robotics Lab

Hosts Albayrak Deniz

Topics Engineering and Technology

Combined Muscle and Nerve Tissue Engineering

Engineered muscle tissues have applications in regenerative medicine, drug testing, and understanding motion. A key challenge is restoring neuromuscular communication, especially in treating volumetric muscle loss (VML). This project aims to create functional neuromuscular constructs with biomimetic innervation. Scaffolds will be made using electrospun fibers, conductive materials, and drug-loaded graphene. Muscle and nerve cells derived from iPSCs will be seeded into these scaffolds. Constructs will be tested for motion, drug response, and integration in bio-hybrid robotic systems. The platform will advance muscle-nerve regeneration, drug testing, and bio-hybrid robotics.

Keywords

Tissue engineering, innervation, neural tissue, nerve, muscle tissue, scaffold, iPSCs, muscle cells, bioprinting, biofabrication, biohybrid robotics, soft robotics, 3D printing, biomaterials, electrical stimulation, actuation.

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Semester Project , Master Thesis

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Published since: 2025-04-06 , Earliest start: 2025-04-06 , Latest end: 2025-09-30

Organization Soft Robotics Lab

Hosts Filippi Miriam

Topics Medical and Health Sciences , Engineering and Technology , Biology

Data-driven/Machine Learning polymer formulation for drug development

This project aims to leverage machine learning to accelerate the design of polymer-based drug delivery systems with tailored release kinetics. Using a curated dataset of polymer formulations and their drug release profiles, predictive models will be developed, validated, and applied to optimize future formulations. By combining computational tools with explainable AI techniques, the project seeks to uncover key design principles and reduce experimental workloads. The outcome will enable smarter, data-driven reformulation processes, advancing personalized medicine and next-generation drug delivery technologies.

Keywords

Data-driven, machine learning, polymer formulation, drug

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Semester Project , Internship

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**Introduction:** Polymer-based drug delivery systems are pivotal in modern medicine, enabling controlled release kinetics that enhance therapeutic efficacy while minimizing side effects. However, traditional formulation development relies heavily on trial-and-error experimentation, which is resource-intensive, time-consuming, and often fails to account for complex interactions between polymer composition, processing parameters, and drug release behavior. This project addresses these limitations by leveraging machine learning, ML, to accelerate the rational design of polymer formulations tailored to specific release profiles. By training predictive models on a curated dataset linking spatial composition, material properties, and kinetic outcomes, this work aims to replace empirical guesswork with data-driven insights. Successful implementation could reduce reformulation timelines for existing drugs by 40–60%, enabling rapid adaptation of therapies for personalized dosing or novel delivery routes. Furthermore, the integration of interpretable ML algorithms, such as Random Forest for feature importance analysis or neural networks for capturing non-linear relationships, promises to uncover previously hidden design rules governing polymer-drug interactions. This approach not only streamlines pharmaceutical development but also advances sustainable practices by minimizing material waste, positioning data-driven formulation as a cornerstone of next-generation smart therapeutics. **Details** In this project, the student will harness the power of machine learning to revolutionize polymer-based drug delivery systems. Using a curated dataset of polymer formulations and their drug release kinetics, they will train predictive models to identify optimal formulations for tailored release profiles. The student will explore cutting-edge algorithms, validate their performance, and use them to design new formulations that minimize experimental guesswork. By combining computational tools with real-world data, the student will accelerate drug reformulation, uncover hidden design principles, and contribute to the future of personalized medicine and smart therapeutics. **Skills and Background Required** We are looking for motivated and independent candidates with a background in data science, chemistry, pharmaceutical sciences, biomedical engineering, bioengineering, or related fields. Previous experience with Data science and machine learning is a plus, a general understanding of polymer chemistry is highly recommended. Familiarity with software like python, R, and ML libraries is needed for model development and optimization. **Join the Project:** This project offers an exciting opportunity to work at the interface of biotechnology, pharmaceutical sciences, and Data-science/machine-learning. Candidates with an interest in translational research and drug development are encouraged to apply.

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Published since: 2025-04-04 , Earliest start: 2025-04-07 , Latest end: 2025-11-30

Organization Macromolecular Engineering Laboratory

Hosts Guzzi Elia

Topics Medical and Health Sciences , Engineering and Technology , Chemistry

Synthesis and characterization of doped-tungsten oxides

If you are interested in the synthesis of nanoparticles, then you are in the right place! Project Description: The project is centered on the microwave-assisted synthesis of tungsten oxides and the characterization of their plasmonic properties. More specifically, the effect of reaction parameters (e.g. reaction temperature, time and pressure, reactants’ concentration, …) on the nanoparticles’ structure, morphology, and optical properties will be investigated. The main characterization techniques will be UV-vis and powder-XRD spectroscopy, together with electron microscopy (SEM, TEM, …) but others will be employed based on need. Environment: You will work in a friendly group, where helping one another is valued and cherished. Supervision style: I will actively teach you how to synthesize and characterize materials. You will also be directly supported throughout the whole project (meaning you will not be left completely alone, and a second explanation can always be given). At the same time, it’s expected that after an introductory period you will be able to work more autonomously and give your contribution to the project.

Keywords

nanoparticle synthesis tungsten oxides plasmonic nanoparticles

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Master Thesis

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Published since: 2025-03-28

Organization Multifunctional Materials

Hosts Radaelli Eleonora

Topics Engineering and Technology , Chemistry

Thesis with Planted: image based product characterization

The goal of this project is to develop an image-based analysis method that enables timely evaluations.

Keywords

plant based meat, extrusion, image based analysis, programming, lab tests

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Semester Project , Bachelor Thesis , Master Thesis

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Published since: 2025-03-27 , Earliest start: 2025-03-30 , Latest end: 2025-11-30

Organization pd|z Product Development Group Zurich

Hosts Reikat Maximilian

Topics Information, Computing and Communication Sciences , Engineering and Technology

Thesis with Planted: lab on a chip

The goal of this project is to develop an analytical method that will enable even small material samples to be tested for their suitability for the extrusion process.

Keywords

plant based meat, extrusion, CAD, design, lab test, lab on a chip

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Semester Project , Bachelor Thesis , Master Thesis

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Published since: 2025-03-27 , Earliest start: 2025-03-30 , Latest end: 2025-11-30

Organization pd|z Product Development Group Zurich

Hosts Reikat Maximilian

Topics Engineering and Technology

Thesis with Planted: CAD-design and Experimentation

Based on pre-study, the aim of this new thesis project is to continue the research work and further improve the die design, fabricate new die variants using AM, and experimentally test them onsite at the facilities of Planted.

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plant based meat, extrusion, CAD, design, lab test,

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Semester Project , Bachelor Thesis , Master Thesis

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Published since: 2025-03-27 , Earliest start: 2025-03-30 , Latest end: 2025-11-30

Organization pd|z Product Development Group Zurich

Hosts Reikat Maximilian

Topics Engineering and Technology

How Mechanical Forces Shape Cell Fate – and the Future of Regenerative Medicine

Project Summary We’re developing a powerful new in vitro model to untangle the complex mechanical cues—osmotic pressure and substrate stiffness—that skin cells experience every day. These signals are deeply intertwined in the body, but we’re building a system to decouple and precisely control them, for the first time. Why? Because understanding how cells respond to these forces is crucial for engineering functional tissues, guiding organ regeneration, and tackling mechanobiology-driven diseases like fibrosis.

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Key words: mechanical stresses, cell behavior, fibroblasts, immunostaining.

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Master Thesis

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Published since: 2025-03-26 , Earliest start: 2025-06-01 , Latest end: 2026-01-31

Organization Macromolecular Engineering Laboratory

Hosts Cuni Filippo

Topics Engineering and Technology , Biology

PDMS-Based Bioreactor Investigating Cell Behavior in Response to Hydrostatic Pressure and Substrate Stiffness

Introduction and Background Skin cells dynamically respond to mechanical and biochemical stimuli, which influence critical processes such as proliferation, differentiation, and migration. By understanding this interplay, mechanical and biochemical stimuli may be used in the future to facilitate the growth of skin patches, tissue formation, and organ regeneration, enabling new therapies and benefiting patients. The study of these responses, mechanobiology, requires advanced in-vitro systems to emulate physiological conditions. This project utilizes a device designed for controlled manipulation of hydrostatic pressure (0.1–1.5 kPa) and substrate stiffness (0.1–100 kPa). The system facilitates isolated and scalable experiments to analyze how the interplay of these mechanical parameters affects cell behavior. In this thesis, the student will use this system to investigate how different stimuli affect cell behavior.

Keywords

Bioreactor, tissue engineering, organ regeneration

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Master Thesis

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Published since: 2025-03-26 , Earliest start: 2025-05-01 , Latest end: 2025-11-30

Organization Macromolecular Engineering Laboratory

Hosts Binz Jonas

Topics Engineering and Technology , Biology

Continual Learning and Domain Adaptation Techniques for a Waste Monitoring System on an Ocean Cleanup Vessel

This thesis develops an automated onboard waste quantification system for a maritime waste collection vessel, leveraging computer vision with continual learning and domain adaptation to replace manual counting of floating waste. Evaluated under real-world maritime conditions, the system aims to improve waste management in the South East Asian Sea.

Keywords

Computer Vision, Continual Learning, Field Testing

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Master Thesis

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Published since: 2025-03-26 , Earliest start: 2025-05-01 , Latest end: 2025-12-31

Organization Robotic Systems Lab

Hosts Stolle Jonas , Elbir Emre

Topics Engineering and Technology

Hardware / software development in medtech startup (project / thesis)

diaxxo, a start-up from ETH Zürich, is transforming molecular diagnostics with an innovative Point-of-Care Polymerase Chain Reaction (PCR) device. Designed to accelerate and democratize access to diagnostic testing, our cutting-edge technology can be used across various fields, from human diagnostics to vet and food testing. Our products are also tailored for use in developing countries and resource-limited settings, aiming to bring reliable diagnostics to every corner of the globe. The company offers several projects and thesis opportunities focusing on interfacing computer and camera systems (e.g. controlling Camera Pi from ESP microcontrollers, and integrating hardware and software components to address design and automation challenges.

Keywords

Point-of-Care PCR, Molecular Diagnostics, Embedded Systems, Arducam SPI, Camera Pi ESP Microcontroller, Hardware-Software Integration, Design and Automation Challenges

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Semester Project , Internship , Bachelor Thesis , Master Thesis

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Published since: 2025-03-26 , Earliest start: 2024-07-01 , Latest end: 2025-06-30

Organization Functional Materials Laboratory

Hosts Gregorini Michele

Topics Information, Computing and Communication Sciences , Engineering and Technology

Extending Functional Scene Graphs to Include Articulated Object States

While traditional [1] and functional [2] scene graphs are capable of capturing the spatial relationships and functional interactions between objects and spaces, they encode each object as static, with fixed geometry. In this project, we aim to enable the estimation of the state of articulated objects and include it in the functional scene graph.

Keywords

scene understanding, scene graph, exploration

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Master Thesis

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Published since: 2025-03-25 , Earliest start: 2025-03-25

Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne

Organization Computer Vision and Geometry Group

Hosts Bauer Zuria, Dr. , Trisovic Jelena , Zurbrügg René

Topics Information, Computing and Communication Sciences , Engineering and Technology

Development of a Heterocellular Human Bone Organoid for Precision Medicine and Treatment

Our goal is to establish a heterocellular 3D printed bone organoid model comprising all major bone cell types (osteoblasts, osteocytes, osteoclasts) to recapitulate bone remodeling units in an in vitro system. The organoids will be produced with the human cells, as they could represent human pathophysiology better than animal models, and eventually could replace them. These in vitro models could be used in the advancement of next-generation personalised treatment strategies. Our tools are different kinds of 3D bioprinting platforms, bio-ink formulations, hydrogels, mol-bioassays, and time-lapsed image processing of micro-CT scans.

Keywords

3D printing, bone organoids, co-culture, bioreactor, hydrogels, drug testing

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Semester Project , Internship , Bachelor Thesis , Master Thesis , ETH Zurich (ETHZ)

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Published since: 2025-03-24 , Earliest start: 2022-08-01 , Latest end: 2025-11-30

Organization Müller Group / Laboratory for Bone Biomechanics

Hosts Steffi Chris

Topics Engineering and Technology , Biology

Develop Dexterous Humanoid Robotic Hands

Design and build dexterous human-like robotic hands with us at the Soft Robotics Lab and the ETH spin-off mimic. We will explore different possibilities of developing design features and sub-systems. The developed features shall be integrated into a fully functional robotic hand and applied to solve practical manipulation challenges.

Keywords

humanoid, robotics, hand, dexterity, soft robotics, actuation, prototyping, modeling and control, mechatronics, biomimetic, design, 3D printing, silicone casting, electronics, machine learning, control

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Semester Project , Master Thesis

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Published since: 2025-03-24 , Earliest start: 2025-01-01 , Latest end: 2026-03-31

Organization Soft Robotics Lab

Hosts Weirich Stefan

Topics Engineering and Technology

Learning LiDAR Registration Correspondences

The goal of this project is to train a neural network to learn to predict geometrically consistent point correspondences for accurate LiDAR odometry.

Keywords

Pointcloud, Lidar, Registration, Odometry, Neural Network, Deep Learning, Machine Learning, Robotics

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Master Thesis

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Published since: 2025-03-24 , Earliest start: 2025-04-01 , Latest end: 2026-02-01

Organization Autonomous Systems Lab

Hosts Tuna Turcan , Pfreundschuh Patrick

Topics Information, Computing and Communication Sciences , Engineering and Technology

Motion Imitation and Control for Humanoid Robot using Diffusion

To achieve effective and natural interaction, humanoid robots may need to closely imitate human motion, which encompasses walking, object manipulation, and environmental interactions. Motion capture data, which captures human motion with high precision, serves as an excellent resource for training robotic systems to replicate human movements. Diffusion models are a class of generative models designed to handle multi-modal distributions, making them highly suitable for complex motion generation tasks. Recent state-of-the-art methods use diffusion to produce human like motions for character animation or to imitate human expert data for controlling robotic arms. The goal of this project is to explore diffusion approaches for imitating motion data from humans to obtain control policies for humanoid robots.

Keywords

RL, Imitation Learning, Robotics, Diffusion

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Master Thesis

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Published since: 2025-03-23 , Earliest start: 2025-01-15 , Latest end: 2025-09-30

Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne

Organization Computational robotics laboratory (Prof. Stelian Coros)

Hosts Zargarbashi Fatemeh

Topics Information, Computing and Communication Sciences , Engineering and Technology

Exploring the Mechanoregulation of Bone Regeneration

In over 100 years, the remarkable ability of bone to adapt to its mechanical environment has been a source of scientific fascination. Bone regeneration has been shown to be highly dependent on the mechanical environment at the fracture site. It has been demonstrated that mechanical stimuli can either accelerate or impede regeneration. Despite the fundamental importance of the mechanical environment in influencing bone regeneration, the molecular mechanisms underlying this phenomenon are complex and poorly understood.

Keywords

Bone, Mechanobiology, Spatial transcriptomics, Gene expression, Finite element modelling, Image processing

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Semester Project , Internship , Bachelor Thesis , Master Thesis

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Published since: 2025-03-23 , Earliest start: 2024-11-01 , Latest end: 2025-08-31

Organization Müller Group / Laboratory for Bone Biomechanics

Hosts Mathavan Neashan

Topics Medical and Health Sciences , Engineering and Technology

6month Master Thesis Project: Dynamic Hydrogels for Cardiac Tissue Regeneration

Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, with myocardial infarction and congenital heart defects posing significant challenges for clinical intervention. Current treatments, such as surgical repair or synthetic implants, often suffer from limitations including inflammation, fibrosis, and lack of integration with native cardiac tissue. Hydrogels have emerged as promising candidates for cardiac tissue engineering due to their high water content, biocompatibility, and ability to mimic the extracellular matrix. However, traditional hydrogels often fail to meet the mechanical demands of the beating heart or lack sufficient bioactivity for effective tissue integration. A new generation of hydrogels—engineered with reversible crosslinks and bioactive ECM-derived polymers—offers an exciting solution for regenerative cardiac therapies. These hydrogels can provide a tunable balance between mechanical strength and bioactivity, making them ideal candidates for cardiac tissue engineering and regeneration. We are seeking a motivated Master student for a six-month research project focusing on the development of a dynamic hydrogel for cardiac tissue regeneration. The main objectives of the project include: - Hydrogel Design & Synthesis: Developing a biopolymeric hydrogel with dynamic crosslinking mechanisms - Mechanical Testing: Evaluating the hydrogel’s mechanical properties under physiological condi-tions - Biocompatibility Assessment: Investigating the material’s cytocompatibility and hemocompatibility The project will involve hands-on work in polymer chemistry, biomaterials characterization, and bioengineering techniques. The student will gain experience in hydrogel synthesis, rheology, mechanical testing, and biological assays, contributing to cutting-edge research with potential biomedical applications.

Keywords

Biomaterials; Hydrogels; Tissue engineering

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Master Thesis

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Published since: 2025-03-20 , Earliest start: 2025-05-01 , Latest end: 2025-12-31

Organization Biomimetic Membranes and Textiles

Hosts Wei Kongchang

Topics Engineering and Technology

Intelligent Micromachines Made from Droplet-Based Factory

We invite applications for a Master's thesis / semester project that focuses on the fabrication of microrobots with custom shapes. Using our developed droplet printing technique, this project will explore how different microrobot shapes, created by different magnetic fields and materials, influence their control behaviors in blood vessels. This research aims to advance biomedical technologies, particularly in targeted drug delivery and minimally invasive procedures.

Keywords

Microrobotics, 4D Printing, Soft Materials, Biomedical Devices

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Semester Project , Master Thesis , Student Assistant / HiWi , ETH Zurich (ETHZ)

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Published since: 2025-03-18 , Earliest start: 2025-06-02

Organization Multiscale Robotics Lab

Hosts Hu Minghan

Topics Engineering and Technology , Chemistry

Laser Engraving and Coloring of Fiber-Reinforced Polymer (FRP) High-Performance Bikes

High-performance bicycle frames made from fiber-reinforced polymers (FRPs) benefit from low weight but also require attractive finishes. Painting adds weight, so laser-based engraving or color modification offers a paint-free alternative. This project aims to identify suitable laser parameters and assess potential damage mechanisms (e.g., delamination, fiber/matrix degradation). The outcome will enable paint-less customization of FRP bikes, supported by microstructural analysis (microscopy, SEM, Raman). The thesis is carried out in cooperation with the company Scott Sports SA.

Keywords

laser manufacturing, laser ablation, engraving, composite structures, Fiber-Reinforced Polymer (FRP), bicycle frames

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Semester Project , Master Thesis , ETH Zurich (ETHZ)

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Published since: 2025-03-18 , Earliest start: 2025-04-01 , Latest end: 2025-12-31

Applications limited to Department of Materials , Department of Mechanical and Process Engineering

Organization Advanced Manufacturing

Hosts Neff Luis

Topics Engineering and Technology

Industrial Design of a Medical Robotic Platform for Autonomous Eye Injections

The purpose of this project is to enhance an existing robotic platform capable of autonomously delivering injections into the human eye. Rather than starting from zero, the student will build upon substantial prior research. The project’s primary focus is on industrial design—making the system both visually appealing and suitable for clinical settings. This includes collaborating with external manufacturers for casting and production, resulting in a polished, user-friendly device for healthcare professionals. In addition, the student will do some coding in Python (specifically for motion planning) to run experiments on porcine and mock-eyes using the updated design.

Keywords

Mechanical design, system integration, industrial design

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ETH Zurich (ETHZ)

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Published since: 2025-03-18

Organization Multiscale Robotics Lab

Hosts Ehmke Claas

Topics Engineering and Technology

Towards Fully 3D-Printed EEG Headsets: Eliminating External Wiring with Embedded Electronics

Multimaterial FDM printing offers exciting potential for directly integrating electronics into structural parts. One promising application is in EEG systems, which monitor brain activity using electrodes. The Ultracortex Mark IV by OpenBCI, for example, uses a 3D-printed frame but relies on external wiring. This thesis aims to redesign the headset to incorporate 3D-printed electronics and test its functionality.

Keywords

3D Printing, 3D Printed Electronics; Design; Testing

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Semester Project , Bachelor Thesis , Master Thesis

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Published since: 2025-03-17 , Earliest start: 2025-01-21 , Latest end: 2026-01-01

Applications limited to ETH Zurich

Organization pd|z Product Development Group Zurich

Hosts Vögerl Johannes

Topics Engineering and Technology

Next-Generation Wire Integration: Automating Electrical Routing in 3D-Printed Components

Novel 3D printing processes directly integrate electrical cables into components, solving many challenges of traditional electrical wires like entanglement, high assembly effort, or high system weight. However, the challenge of efficiently designing these integrated wires remains. This thesis aims to automate the design process of these integrated wires enabling fast generation of the integrated electronics in various use cases. Join us in pushing the boundaries for the fast design of complex 3D printing cables.

Keywords

Design Automation, 3D Printing, 3D Printed Electronics, Wiring; Programming

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Semester Project , Bachelor Thesis , Master Thesis , ETH Zurich (ETHZ)

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Published since: 2025-03-17 , Earliest start: 2025-01-21 , Latest end: 2026-01-01

Applications limited to ETH Zurich

Organization pd|z Product Development Group Zurich

Hosts Vögerl Johannes

Topics Engineering and Technology

Simulation of Multi Material Ceramic Laser Ablation

Laser ablation is mainly driven by the so-called laser-matter interaction. Especially for hard-to-machine materials like ceramics, which are gaining increasing attention in technical applications, the ablation phenomenon must be fully understood and controlled to achieve precise and selective material removal while maintaining material integrity. These materials have outstanding properties for a plurality of applications in industry; however, one is faced with the issue of finding a suitable manufacturing technique. Brittleness, hardness, and their tendency to crack lead to complications that could be addressed by the implementation of laser technology. However, experimental approaches alone are not expedient to fully handle and understand the complex phenomenon of laser ablation of ceramic multi materials. This is the motivation for introducing the first steps of simulating laser-matter interaction in multi-material ceramics within this project.

Keywords

Laser, simulation, ceramics, ablation, biomimicry

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Semester Project , Internship , Bachelor Thesis , Master Thesis

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Published since: 2025-03-17 , Earliest start: 2025-04-01 , Latest end: 2025-12-31

Organization Advanced Manufacturing Laboratory

Hosts Reichel Andreas

Topics Engineering and Technology

Human wearing trial in collaboration with industry partner: Impact of protective coveralls on heat strain dur-ing rest and physical activity in warm conditions

To protect from chemical, biological, radioactive, and nuclear (CBRN) contaminants, disposable coveralls are used as personal protective clothing (PPC). In addition to its protective nature, PPC affects heat dissemination from the body, resulting in increased heat strain, which might result in reduced working performance. Therefore, new clothing material developments aim to reduce thermal load while maintaining a protective nature. We conduct a wearing trial in collaboration with an industrial partner to investigate the impact of different types of protective coveralls on heat strain during exposure to warm conditions, including resting phases and physical activity.

Keywords

Personal protective clothing, heat strain, human subject trials, sweating, sensors, data analysis.

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Semester Project , Internship , Lab Practice , Master Thesis

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Published since: 2025-03-16 , Earliest start: 2025-05-01 , Latest end: 2026-01-31

Organization Biomimetic Membranes and Textiles

Hosts Annaheim Simon

Topics Medical and Health Sciences , Engineering and Technology , Biology

Event-based feature detection for highly dynamic tracking

Event cameras are an exciting new technology enabling sensing of highly dynamic content over a broad range of illumination conditions. The present thesis explores novel, sparse, event-driven paradigms for detecting structure and motion patterns in raw event streams.

Keywords

Event camera, neuromorphic sensing, feature detection, computer vision

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Master Thesis

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Event cameras are a relatively new, vision-based exteroceptive sensor relying on standard CMOS technology. Unlike normal cameras, event cameras do not measure absolute brightness in a frame-by-frame manner, but relative changes of the pixel-level brightness. Essentially, every pixel of an event camera independently observes the local brightness pattern, and when the latter experiences a relative change of minimum amount with respect to a previous value, a measurement is triggered in the form of a time-stamped event indicating the image location as well as the polarity of the change (brighter or darker) [2]. The pixels act asynchronously and can potentially fire events at a very high rate. Owing to their design, event cameras do not suffer from the same artifacts as regular cameras, but continue to perform well under high dynamics or challenging illumination conditions. Event cameras currently enjoy growing popularity and they represent a new, interesting alternative for exteroceptive sensing in robotics when facing scenarios with high dynamics and/or challenging conditions. The focus of the present thesis lies on 3D motion estimation with event cameras, and in particular aims at event-driven, computationally efficient methods that can trigger motion hypotheses from sparse raw events. Initial theoretical advances in this direction have been presented in recent literature [3,4,5], though these methods are still limited in terms of the assumptions that they make. The present thesis will push the boundaries by proposing novel both geometry and learning-based representations. The proposed thesis will be conducted at the Robotics and AI Institute, a new top-notch partner institute of Boston Dynamics pushing the boundaries of control and perception in robotics. Selection is highly competitive. Potential candidates are invited to submit their CV and grade sheet, after which students will be invited to an on-site interview. [1] Event-Based, 6-DOF Camera Tracking from Photometric Depth Maps, TPAMI 40(10):2402-2412, 2017 [2] The Silicon Retina, 264(5): 76-83, 1991 [3] A 5-Point Minimal Solver for Event Camera Relative Motion Estimation. In Proceedings of the International Conference on Computer Vision (ICCV), 2023 [4] An n-point linear solver for line and motion estimation with event cameras. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024. [5] Full-DoF Egomotion Estimation for Event Cameras Using Geometric Solvers, Arxiv: https://arxiv.org/html/2503.03307v1

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Published since: 2025-03-13 , Earliest start: 2025-03-17

Organization Robotic Systems Lab

Hosts Kneip Laurent

Topics Engineering and Technology

Fast, change-aware map-based camera tracking

Experiment with Gaussian Splatting based map representations for highly efficient camera tracking and simultaneous change detection and map updating. Apply to different exteroceptive sensing modalities.

Keywords

Localization, Camera Tracking, Gaussian Splatting, Change detection

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Master Thesis

Description

Novel techniques for 3D environment representation such as NeRF [2] and Gaussian Splatting [1,3] provide the ability to efficiently render realistically looking images of environments, and-if formulated as a differentiable function of camera pose-can be embedded into a photometric loss in order to enable camera tracking across different modalities such as RGB cameras and event cameras. However, such representations are by default not able to accommodate for changes in the scene, which may happen in many practically relevant scenarios (e.g. domestic environment). Furthermore, in some of the relevant scenarios, the changes that occur over time may indeed be expected and according to plan (e.g. construction environment). The present thesis looks into recent 3D reconstruction methods such as Gaussian Spatting and considers their use for real-time vision-based sensor tracking. However, rather than relying on a static map of the environment, the core of the method consists of incorporating a robust change detection and map updating mechanism that relies on a combination of measurement residuals and available priors. The final goal will be to enable long-term vision-based localization in gradually changing environments while simultaneously making use of new sensing data to update the map. The ultimate goal will be to extend the method to sensors that excel at highly dynamic motion tracking, but do not necessarily represent our first choice when thinking of a mapping device (i.e. event cameras). The proposed thesis will be conducted at the Robotics and AI Institute, a new top-notch partner institute of Boston Dynamics pushing the boundaries of control and perception in robotics. Selection is highly competitive. Potential candidates are invited to submit their CV and grade sheet, after which students will be invited to an on-site interview. [1] GS-EVT: Cross-Modal Event Camera Tracking based on Gaussian Splatting. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2025 [2] NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis, 2020, Arxiv: https://arxiv.org/abs/2003.08934 [3] 3D Gaussian Splatting for Real-Time Radiance Field Rendering, 2023, Arxiv: https://arxiv.org/abs/2308.04079

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Published since: 2025-03-13 , Earliest start: 2025-03-17

Organization Robotic Systems Lab

Hosts Kneip Laurent

Topics Engineering and Technology

Soft object reconstruction

This project consists of reconstructing soft object along with their appearance, geometry, and physical properties from image data for inclusion in reinforcement learning frameworks for manipulation tasks.

Keywords

Computer Vision, Structure from Motion, Image-based Reconstruction, Physics-based Reconstruction

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Master Thesis

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As 3D reconstruction [2,3], real-time data-driven rendering [4,5], and learning-based control technologies [6,7] are becoming more mature, recent efforts in reinforcement learning are moving towards end-to-end policies that directly consume images in order to generate control commands [8]. However, many of the simulated environments are limited to a composition of rigid objects. In recent years, the inclusion of differentiable particle-based simulation borrowed from computer graphics has enabled the inclusion of non-rigid or even fluid elements. Ideally, we can generate such representations from real world data in order to extend data-driven world simulators to arbitrary new objects with complex physical behavior. The present thesis focuses on this problem and aims at reconstructing soft objects in terms of their geometry, appearance, and physical behavior. The goal is to make use of the Material Point Method (MPM) in combination with vision-based cues and physical priors in order to reconstruct accurate 3D models of soft objects. The developed models will finally be included into an RL learning environment such as Isaac-Gym in order to train novel manipulation policies for soft objects. The proposed thesis will be conducted at the Robotics and AI Institute, a new top-notch partner institute of Boston Dynamics pushing the boundaries of control and perception in robotics. Selection is highly competitive. Potential candidates are invited to submit their CV and grade sheet, after which students will be invited to an on-site interview. [1] Modeling of Deformable Objects for Robotic Manipulation: A Tutorial and Review, Front. Robot. AI, 7, 2020 [2] Global Structure-from-Motion Revisited, ECCV 2024 [3] MASt3R-SLAM: Real-Time Dense SLAM with 3D Reconstruction Priors, CVPR 2025 [4] NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis, CVPR 2020 [5] 3D Gaussian Splatting for Real-Time Radiance Field Rendering, SIGGRAPH 2023 [6] Learning to walk in minutes using massively parallel deep reinforcement learning, CoRL 2022 [7] Champion-Level Drone Racing Using Deep Reinforcement Learning, Nature, 2023 [8] π0: A Vision-Language-Action Flow Model for General Robot Control, Arxiv: https://arxiv.org/abs/2410.24164

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Published since: 2025-03-13 , Earliest start: 2025-03-17

Organization Robotic Systems Lab

Hosts Kneip Laurent

Topics Engineering and Technology

Reconstruction from online videos taken in the wild

Push the limits of arbitrary online video reconstruction by combining the most recent, prior-supported real-time Simultaneous Localization And Mapping (SLAM) methods with automatic supervision techniques.

Keywords

Computer Vision, 3D Reconstruction, SLAM

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Master Thesis

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Published since: 2025-03-13 , Earliest start: 2025-03-17

Organization Robotic Systems Lab

Hosts Kneip Laurent

Topics Engineering and Technology

Computationally Efficient Neural Networks

Computing, time, and energy requirements of recent neural networks have demonstrated dramatic increase over time, impacting on their applicability in real-world contexts. The present thesis explores novel ways of implementing neural network implementations that will substantially reduce their computational complexity and thus energy footprint.

Keywords

AI, CNNs, transformers, network implementation

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Master Thesis

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Over the past decade, advances in deep learning and computer vision have led to substantial improvements in robotic perception abilities. It is nowadays possible to use neural networks for reliable object detection, object pose and shape estimation, open-vocabulary semantic interpretation, and the solution of low-level problems such as feature tracking and depth estimation, to name just a few. However, a limiting factor of growing concern is the computational complexity and thus the power consumption/computing hardware vs. latency trade-off of such models. We are therefore also experiencing an increasing demand for cloud-based computation, often with remaining and unpredictable latencies. As demonstrated through a number of past efforts [2,3,4], the computational complexity of a neural network can be reduced fairly substantially by changes in the selected low-level computing paradigm. Rather than relying on standard matrix-vector multiplications that make use of hardware multipliers, we can choose architectures that will rely more on additive operations [2], thereby reducing computational complexity and thus energy consumption by a substantial amount. The present work aims at the development and testing of novel and efficient network implementations that can be applied to any off-the-shelf network when deployed in custom-programmable hardware. The proposed thesis will be conducted at the Robotics and AI Institute, a new top-notch partner institute of Boston Dynamics pushing the boundaries of control and perception in robotics. Selection is highly competitive. Potential candidates are invited to submit their CV and grade sheet, after which students will be invited to an on-site interview. [1] On global electricity usage of communication technology: Trends to 2030, Challenges 6(1), 117-157, 2015 [2] The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits, https://arxiv.org/pdf/2402.17764 [3] XOR-Net: An Efficient Computation Pipeline for Binary Neural Network Inference on Edge Devices, https://cmu-odml.github.io/papers/XOR-Net_An_Efficient_Computation_Pipeline_for_Binary_Neural_Network_Inference_on_Edge_Devices.pdf [4] DeepSeek-V3 Technical Report, https://arxiv.org/abs/2412.19437

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Published since: 2025-03-12 , Earliest start: 2025-03-17

Organization Robotic Systems Lab

Hosts Kneip Laurent

Topics Engineering and Technology

Microfabrication and characterization of microelectrodes for neural interfaces

The focus of this project is on the fabrication of microelectrodes for advanced neural interfaces in a cutting-edge cleanroom environment. Our research aims to develop high-resolution neural probes utilizing state-of-the-art microfabrication techniques.

Keywords

Microfabrication; Cleanroom; Neural Interfaces; Microelectrode Arrays; Biomedical Devices; Nanotechnology; Electrochemical Deposition; Neurotechnology; Semiconductor Processing; Bioengineering.

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Semester Project , Master Thesis

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Published since: 2025-03-12 , Earliest start: 2023-12-01

Organization Bio Engineering Laboratory

Hosts Cardes Fernando

Topics Engineering and Technology

Design of analog front-end for CMOS-based neural interfaces

This project focuses on the design of low-noise, low-power, compact amplifiers for next-generation neural interfaces.

Keywords

Microelectronics, VLSI, CMOS, LNA, electronics, analog integrated circuit design, neural interface.

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Semester Project , Master Thesis

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Published since: 2025-03-12 , Earliest start: 2024-07-01

Organization Bio Engineering Laboratory

Hosts Cardes Fernando

Topics Engineering and Technology

Improvements of the Detection Block of a Rapid PCR diagnostic device in a medtech startup

diaxxo, a start-up from ETH Zürich, is transforming molecular diagnostics with an innovative Point-of-Care Polymerase Chain Reaction (PCR) device. This project aims to enhance the detection module of Diaxxo's Point-of-Care Polymerase Chain Reaction (PCR) system by improving light uniformity to ensure precise and reliable optical detection. The redesigned detection block will be compatible with Diaxxo's existing cartridges and support multiplexing with up to three distinct light channels. High light uniformity across the cartridge will be a key focus to enhance detection accuracy and reliability. Additionally, the project emphasizes cost-effective design, leveraging affordable materials and methods to maintain performance without increasing production costs.

Keywords

Optical Detection Fluorescence-Based Methods Light Uniformity Fiber Optics Multiplexing PCR Diagnostics Thermocycling Cost-Effective Design Point-of-Care Systems Molecular Diagnostics

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Semester Project , Bachelor Thesis , Master Thesis

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Polymerase Chain Reaction (PCR) diagnostics is a powerful molecular technique used to amplify and detect specific DNA sequences, enabling the precise identification of pathogens and genetic conditions. The process relies on thermocycling, a method of repeatedly heating and cooling DNA samples to facilitate DNA denaturation, primer annealing, and extension. This amplification process exponentially increases the target DNA sequence, making it detectable and quantifiable. A critical component of modern PCR diagnostics is the optical detection module, which monitors DNA amplification in real time. This is achieved using fluorescence-based methods, where fluorescent dyes bind to the amplified DNA, and their emitted light is detected and measured. Optical detection provides exceptional sensitivity and specificity, making it indispensable for the rapid and accurate analysis of PCR results. The primary objective of this project is to enhance the detection module of Diaxxo's Point-of-Care PCR system. The current focus is on achieving better light uniformity, a crucial factor in ensuring the accuracy and reliability of the optical detection process. The redesigned detection block will need to meet the following key requirements: - Compatibility: The new design must be fully compatible with Diaxxo's existing cartridges to ensure seamless integration. - Multiplexing: The module must support up to three distinct light channels, enabling the detection of multiple targets in a single run. - High Light Uniformity: Uniform light distribution across the entire cartridge is essential to improve the precision and consistency of fluorescence detection. - Cost-Effectiveness: The detection block must be designed using cost-effective materials and methods to maintain affordability without compromising performance. By addressing these requirements, this project will significantly enhance the performance of Diaxxo's PCR diagnostic platform, making it more reliable and accessible for diverse applications. It offers an excellent opportunity for innovation in optical detection technology, providing a great experience in developing cutting-edge solutions for molecular diagnostics.

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Published since: 2025-03-11 , Earliest start: 2025-01-01 , Latest end: 2025-09-01

Organization Functional Materials Laboratory

Hosts Gregorini Michele

Topics Medical and Health Sciences , Engineering and Technology , Physics

Generalist Excavator Transformer

We want to develop a generalist digging agent that is able to do multiple tasks, such as digging and moving loose soil, and/or control multiple excavators. We plan to use decision transformers, trained on offline data, to accomplish these tasks.

Keywords

Offline reinforcement learning, transformers, autonomous excavation

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Published since: 2025-03-11 , Earliest start: 2025-03-01 , Latest end: 2025-08-31

Organization Robotic Systems Lab

Hosts Werner Lennart , Egli Pascal Arturo , Terenzi Lorenzo , Nan Fang , Zhang Weixuan

Topics Information, Computing and Communication Sciences

Exploring the 3D Mineralization Behavior in Material-Induced Osteoinduction Through a Multiscale Micro-CT Imaging Approach

The project aims at investigating material-induced osteoinduction using the available mouse model of orthotopic or ectopic bone graft substitute (BGS) application. Through the 3D-3D registration of ex vivo and in vivo multiscale micro-CT images, crucial 3D mineralization behavior of the BGS can be investigated.

Keywords

Femur, Bone Graft Substitute, Critical Size Defect, Osteoinduction, in vivo, micro-CT, 3D-3D Image Registration, Image Analysis, Image Processing, Python, Computational

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Semester Project , Bachelor Thesis , Master Thesis

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Bone defects that do not heal spontaneously despite surgical stabilization and require further surgical intervention are termed critical sized defects (CSD). They pose dramatic consequences to a person’s well-being and life expectancy due to their impact on the skeletal system’s integrity. Bone graft substitutes (BGS) present a treatment approach by enhancing and facilitating the natural healing capacity of the bone tissue using their osteoconductive and osteoinductive potential and filling up the physical space of lost bone for mechanical support. The key property of a BGS in treatment of a large bone defect is osteoinduction. Physiological processes in bone usually happen on different spatial scales, spanning from the whole organ, to tissue, to cellular and even to the molecular level. Therefore, an ideal imaging setup to **unravel the underlying mechanisms of material-induced osteoinduction** would require a **multiscale approach** to connect orang and tissue level processes with the cellular and subcellular scale. Micro-CT can be considered the gold standard in hierarchical 3D imaging of bone structure and function and allows a non-destructive _ex vivo_ and _in vivo_ image acquisition. **Time-lapsed _in vivo_ micro-CT images** can reveal distinct information on the longitudinal development of implant calcification or resorption. However, its resolution is relatively low and certain details can not be detected. _Ex vivo_ micro-CT on the other hand allows scans of higher resolution and therefore complements the _in vivo_ micro-CT well. The 3D-3D registration of high-resolution _ex vivo_ and low-resolution _in vivo_ micro-CT images generates a multiscale micro-CT imaging approach. Advanced image analysis methods can help identify newly formed bone and generally distinguish bone and the BGS. By combined analysis of multiscale micro-CT measurements of murine femur samples with implanted ectopic or orthotopic BGS, critical 3D mineralization behavior in material-induced osteoinduction can be investigated.

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Published since: 2025-03-11 , Earliest start: 2025-04-01 , Latest end: 2026-01-31

Organization Müller Group / Laboratory for Bone Biomechanics

Hosts Lindenmann Sara

Topics Medical and Health Sciences , Engineering and Technology

Differential Particle Simulation for Robotics

This project focuses on applying differential particle-based simulation to address challenges in simulating real-world robotic tasks involving interactions with fluids, granular materials, and soft objects. Leveraging the differentiability of simulations, the project aims to enhance simulation accuracy with limited real-world data and explore learning robotic control using first-order gradient information.

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Published since: 2025-03-10 , Earliest start: 2025-01-01 , Latest end: 2025-12-31

Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne

Organization Robotic Systems Lab

Hosts Nan Fang , Ma Hao

Topics Engineering and Technology

PCB design for CNT-FET Gas Sensor Array

In our project, the objective is to detect nitrogen dioxide gas (NO2) through Carbon Nanotube (CNT) gas sensor array by repeatedly measuring the transfer characteristics of the CNT-FETs (Field Effect Transistors) in the array. However, achieving sensor recovery poses a challenge, necessitating the application of high voltage pulses at regular intervals to heat the channel and desorb gas molecules (self-heating). Our current measurement mode face challenges in independently controlling and tuning the voltages for each device. This project aims to develop a new PCB design that addresses these challenges.

Keywords

carbon nanotube, CNTFET, PCB design, electronics

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Semester Project , Master Thesis

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Published since: 2025-03-10 , Earliest start: 2024-11-10 , Latest end: 2025-06-01

Organization Chair of Micro and Nanosystems

Hosts Gentili Cristina

Topics Engineering and Technology

Wearable kirigami antenna for motion monitoring

The aim of the project is to develop a simple method for fabrication of kirigami-inspired laser-cut or molded antennas on flexible substrates. This technology will enable advancements in wearable electronics for wireless communication and sensing applications.

Keywords

wearable, flexible electronics, kirigami, laser cutting, 3D printing, antenna design, conductivity, wireless communication

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Semester Project , Bachelor Thesis

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Published since: 2025-03-09 , Earliest start: 2025-03-24 , Latest end: 2026-08-31

Organization Biomedical and Mobile Health Technology Lab

Hosts Kateb Pierre

Topics Engineering and Technology

Soft Robot Under Ultrasound Actuation

Soft robotics has gained significant attention in recent years due to its ability to perform delicate and adaptive tasks that traditional rigid robots cannot achieve. Among various actuation methods, ultrasound-driven soft robots present a promising avenue for non-contact and precise control in liquid environments. These robots have potential applications in biomedical fields, microfluidics, and underwater exploration. The integration of ultrasound as an actuation mechanism enables efficient propulsion, controlled deformation, and complex locomotion patterns.

Keywords

ultrasound-driven soft robots

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Master Thesis

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Published since: 2025-03-07 , Earliest start: 2025-03-10 , Latest end: 2025-09-10

Organization Acoustic Robotics for Life Sciences and Healthcare (ARSL)

Hosts Zhong Chengxi

Topics Engineering and Technology , Physics

Development of intelligent lab-on-a-chip devices for high-throughput cell manipulation and microrobot production

Microfluidic devices can be employed in biological research as lab-on-a-chip (LoC) and organ-on-a-chip (OoC) systems. These platforms enable precise in-situ cell manipulation within a highly controlled environment. In the project, we aim to develop an intelligent LoC/OoC device featuring a flexible smart “switch”, for massive production of biohybrid microrobots and high-throughput cell manipulation and drug testing.

Keywords

Lab-on-a-chip, organ-on-a-chip, magnetic microrobot, cell manipulation

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Semester Project , Bachelor Thesis , Master Thesis

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Published since: 2025-03-05 , Earliest start: 2025-03-10 , Latest end: 2025-12-31

Applications limited to ETH Zurich , Empa , EPFL - Ecole Polytechnique Fédérale de Lausanne , University of Basel , University of Berne , University of Fribourg , University of Geneva , University of Lausanne , University of Lucerne , University of St. Gallen , University of Zurich , Zurich University of Applied Sciences , Zurich University of the Arts , Hochschulmedizin Zürich , Lucerne University of Applied Sciences and Arts

Organization Multiscale Robotics Lab

Hosts Zhu Jiawei

Topics Engineering and Technology , Biology

Novel Winch Control for Robotic Climbing

While legged robots have demonstrated impressive locomotion performance in structured environments, challenges persist in navigating steep natural terrain and loose, granular soil. These challenges extend to extraterrestrial environments and are relevant to future lunar, martian, and asteroidal missions. In order to explore the most extreme terrains, a novel winch system has been developed for the ANYmal robot platform. The winch could potentially be used as a fail-safe device to prevent falls during unassisted traverses of steep terrain, as well as an added driven degree of freedom for assisted ascending and descending of terrain too steep for unassisted traversal. The goal of this project is to develop control policies that utilize this new hardware and enable further climbing robot research.

Keywords

Robot, Space, Climbing, Winch, Control

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Semester Project , Master Thesis , ETH Zurich (ETHZ)

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Published since: 2025-03-05 , Earliest start: 2024-10-07

Organization Robotic Systems Lab

Hosts Vogel Dylan

Topics Information, Computing and Communication Sciences , Engineering and Technology

Beyond Value Functions: Stable Robot Learning with Monte-Carlo GRPO

Robotics is dominated by on-policy reinforcement learning: the paradigm of training a robot controller by iteratively interacting with the environment and maximizing some objective. A crucial idea to make this work is the Advantage Function. On each policy update, algorithms typically sum up the gradient log probabilities of all actions taken in the robot simulation. The advantage function increases or decreases the probabilities of these taken actions by comparing their “goodness” versus a baseline. Current advantage estimation methods use a value function to aggregate robot experience and hence decrease variance. This improves sample efficiency at the cost of introducing some bias. Stably training large language models via reinforcement learning is well-known to be a challenging task. A line of recent work [1, 2] has used Group-Relative Policy Optimization (GRPO) to achieve this feat. In GRPO, a series of answers are generated for each query-answer pair. The advantage is calculated based on a given answer being better than the average answer to the query. In this formulation, no value function is required. Can we adapt GRPO towards robot learning? Value Functions are known to cause issues in training stability [3] and a result in biased advantage estimates [4]. We are in the age of GPU-accelerated RL [5], training policies by simulating thousands of robot instances simultaneously. This makes a new monte-carlo (MC) approach towards RL timely, feasible and appealing. In this project, the student will be tasked to investigate the limitations of value-function based advantage estimation. Using GRPO as a starting point, the student will then develop MC-based algorithms that use the GPU’s parallel simulation capabilities for stable RL training for unbiased variance reduction while maintaining a competitive wall-clock time.

Keywords

Robot Learning, Reinforcement Learning, Monte Carlo RL, GRPO, Advantage Estimation

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Semester Project , Bachelor Thesis , Master Thesis

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Published since: 2025-03-05

Organization Robotic Systems Lab

Hosts Klemm Victor

Topics Information, Computing and Communication Sciences , Engineering and Technology , Behavioural and Cognitive Sciences

Magnetic microrobots for targeted therapies

Recent advancements in medical robotics have significantly enhanced precision and innovation in healthcare, indicating a new era of minimally invasive procedures that reduce patient complications and discomfort. In particular, the development of robots constructed from materials with elastic moduli similar to soft biological tissues offers substantial promise for biomedical applications, as their inherent mechanical compliance improves safety during procedures. This project aims to fabricate double-compartment, soft, tether-free magnetic microrobots and to investigate their locomotion strategies. Critical parameters, including shape and magnetic anisotropy, will be examined in detail. Finally, the prototypes will be loaded with a model drug and evaluated in vitro using a silicone model.

Keywords

Nanoparticles, Iron Oxide, Hydrogel, Magnetic Navigation, Anisotropy

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Semester Project , Bachelor Thesis , Master Thesis , ETH Zurich (ETHZ)

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Published since: 2025-03-04 , Earliest start: 2025-03-09 , Latest end: 2025-08-31

Applications limited to ETH Zurich

Organization Multiscale Robotics Lab

Hosts Pustovalov Vitaly

Topics Engineering and Technology , Earth Sciences , Chemistry

Synthesis of a novel monomer for polymeric materials with on-demand degradation and enhanced durability

Plastic waste and the resulting environmental pollution are major challenges of our time. One of the problems is the mismatch of degradability and durability in plastics. Single use plastics like packaging material should be easy to degrade to facilitate recycling after use. However, these single use plastics are often very stable and hard to recycle. Performance plastics need to last during their lifetime without significant decrease in material properties, but aging in these materials eventually leads to material failure and replacement. Both situations generate plastic waste. Therefore, we want to synthesize a material that can degrade on-demand and experiences enhanced durability on longer timescales to satisfy the needs of single use plastics and performance plastics, respectively.

Keywords

Organic and polymer chemistry, novel monomer, degradability and durability

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Semester Project , Master Thesis

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Published since: 2025-03-04 , Earliest start: 2025-04-01

Organization Macromolecular Engineering Laboratory

Hosts Söll Carolina

Topics Chemistry

Deep Learning-Based Blood Smear Scanning System for Automated White Blood Cell Classification

This project aims to develop a deep learning-powered microscope capable of imaging and classifying various white blood cell (WBC) subtypes from blood smears

Keywords

machine learning, white blood cell classification, deep learning, microscopy, image processing

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Published since: 2025-03-04 , Earliest start: 2025-03-01

Organization Biochemical Engineering (deMello Group)

Hosts Aslan Mahmut

Topics Engineering and Technology

Deep Learning of Residual Physics For Soft Robot Simulation

Incorporating state-of-the-art deep learning approaches to augment conventional soft robotic simulations for a fast, accurate and useful simulation for real soft robots.

Keywords

Soft Robotics, Machine Learning, Physical Modeling, Simulation

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Published since: 2025-03-02 , Earliest start: 2025-03-01 , Latest end: 2026-03-01

Organization Soft Robotics Lab

Hosts Michelis Mike , Katzschmann Robert, Prof. Dr.

Topics Information, Computing and Communication Sciences , Engineering and Technology

Volumetric Bucket-Fill Estimation

Gravis Robotics is an ETH spinoff from the Robotic Systems Lab (RSL) working on the automation of heavy machinery (https://gravisrobotics.com/). In this project, you will be working with the Gravis team to develop a perceptive bucket-fill estimation system. You will conduct your project at Gravis under joint supervision from RSL.

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Autonomous Excavation

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Published since: 2025-02-28 , Earliest start: 2025-01-01 , Latest end: 2026-01-01

Organization Robotic Systems Lab

Hosts Egli Pascal Arturo

Topics Engineering and Technology

Leveraging Human Motion Data from Videos for Humanoid Robot Motion Learning

The advancement in humanoid robotics has reached a stage where mimicking complex human motions with high accuracy is crucial for tasks ranging from entertainment to human-robot interaction in dynamic environments. Traditional approaches in motion learning, particularly for humanoid robots, rely heavily on motion capture (MoCap) data. However, acquiring large amounts of high-quality MoCap data is both expensive and logistically challenging. In contrast, video footage of human activities, such as sports events or dance performances, is widely available and offers an abundant source of motion data. Building on recent advancements in extracting and utilizing human motion from videos, such as the method proposed in WHAM (refer to the paper "Learning Physically Simulated Tennis Skills from Broadcast Videos"), this project aims to develop a system that extracts human motion from videos and applies it to teach a humanoid robot how to perform similar actions. The primary focus will be on extracting dynamic and expressive motions from videos, such as soccer player celebrations, and using these extracted motions as reference data for reinforcement learning (RL) and imitation learning on a humanoid robot.

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Published since: 2025-02-25

Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne

Organization ETH Competence Center - ETH AI Center

Hosts Li Chenhao , Kaufmann Manuel , Li Chenhao , Li Chenhao , Kaufmann Manuel , Li Chenhao

Topics Engineering and Technology

Learning Agile Dodgeball Behaviors for Humanoid Robots

Agility and rapid decision-making are vital for humanoid robots to safely and effectively operate in dynamic, unstructured environments. In human contexts—whether in crowded spaces, industrial settings, or collaborative environments—robots must be capable of reacting to fast, unpredictable changes in their surroundings. This includes not only planned navigation around static obstacles but also rapid responses to dynamic threats such as falling objects, sudden human movements, or unexpected collisions. Developing such reactive capabilities in legged robots remains a significant challenge due to the complexity of real-time perception, decision-making under uncertainty, and balance control. Humanoid robots, with their human-like morphology, are uniquely positioned to navigate and interact with human-centered environments. However, achieving fast, dynamic responses—especially while maintaining postural stability—requires advanced control strategies that integrate perception, motion planning, and balance control within tight time constraints. The task of dodging fast-moving objects, such as balls, provides an ideal testbed for studying these capabilities. It encapsulates several core challenges: rapid object detection and trajectory prediction, real-time motion planning, dynamic stability maintenance, and reactive behavior under uncertainty. Moreover, it presents a simplified yet rich framework to investigate more general collision avoidance strategies that could later be extended to complex real-world interactions. In robotics, reactive motion planning for dynamic environments has been widely studied, but primarily in the context of wheeled robots or static obstacle fields. Classical approaches focus on precomputed motion plans or simple reactive strategies, often unsuitable for highly dynamic scenarios where split-second decisions are critical. In the domain of legged robotics, maintaining balance while executing rapid, evasive maneuvers remains a challenging problem. Previous work on dynamic locomotion has addressed agile behaviors like running, jumping, or turning (e.g., Hutter et al., 2016; Kim et al., 2019), but these movements are often planned in advance rather than triggered reactively. More recent efforts have leveraged reinforcement learning (RL) to enable robots to adapt to dynamic environments, demonstrating success in tasks such as obstacle avoidance, perturbation recovery, and agile locomotion (Peng et al., 2017; Hwangbo et al., 2019). However, many of these approaches still struggle with real-time constraints and robustness in high-speed, unpredictable scenarios. Perception-driven control in humanoids, particularly for tasks requiring fast reactions, has seen advances through sensor fusion, visual servoing, and predictive modeling. For example, integrating vision-based object tracking with dynamic motion planning has enabled robots to perform tasks like ball catching or blocking (Ishiguro et al., 2002; Behnke, 2004). Yet, dodging requires a fundamentally different approach: instead of converging toward an object (as in catching), the robot must predict and strategically avoid the object’s trajectory while maintaining balance—often in the presence of limited maneuvering time. Dodgeball-inspired robotics research has been explored in limited contexts, primarily using wheeled robots or simplified agents in simulations. Few studies have addressed the challenges of high-speed evasion combined with the complexities of humanoid balance and multi-joint coordination. This project aims to bridge that gap by developing learning-based methods that enable humanoid robots to reactively avoid fast-approaching objects in real time, while preserving stability and agility.

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Published since: 2025-02-25

Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne

Organization ETH Competence Center - ETH AI Center

Hosts Li Chenhao , Li Chenhao , Li Chenhao , Li Chenhao

Topics Engineering and Technology

Learning Real-time Human Motion Tracking on a Humanoid Robot

Humanoid robots, designed to mimic the structure and behavior of humans, have seen significant advancements in kinematics, dynamics, and control systems. Teleoperation of humanoid robots involves complex control strategies to manage bipedal locomotion, balance, and interaction with environments. Research in this area has focused on developing robots that can perform tasks in environments designed for humans, from simple object manipulation to navigating complex terrains. Reinforcement learning has emerged as a powerful method for enabling robots to learn from interactions with their environment, improving their performance over time without explicit programming for every possible scenario. In the context of humanoid robotics and teleoperation, RL can be used to optimize control policies, adapt to new tasks, and improve the efficiency and safety of human-robot interactions. Key challenges include the high dimensionality of the action space, the need for safe exploration, and the transfer of learned skills across different tasks and environments. Integrating human motion tracking with reinforcement learning on humanoid robots represents a cutting-edge area of research. This approach involves using human motion data as input to train RL models, enabling the robot to learn more natural and human-like movements. The goal is to develop systems that can not only replicate human actions in real-time but also adapt and improve their responses over time through learning. Challenges in this area include ensuring real-time performance, dealing with the variability of human motion, and maintaining stability and safety of the humanoid robot.

Keywords

real-time, humanoid, reinforcement learning, representation learning

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Published since: 2025-02-25

Organization ETH Competence Center - ETH AI Center

Hosts Li Chenhao , Li Chenhao , Li Chenhao , Li Chenhao

Topics Information, Computing and Communication Sciences

Acoustic Standing Waves for Particle Manipulation in Air

Acoustic standing waves can be used to manipulate physical objects in both gas and liquid environments. This project investigates their effects on particle flow and selectivity in air, considering various particle sizes and weights. Through modeling, simulations, and experimental validation, we aim to characterize the selectivity of these waves and develop a compact driver circuit for practical implementation. The student will work closely with Honeywell engineers on test setups, electronic designs, and prototyping. A successful outcome may lead to a subsequent R&D phase or PhD project in a collaboration with Honeywellto further develop these findings.

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Acoustics, Standing Waves, Particle Manipulation, Flow Control, Electronics

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Published since: 2025-02-25 , Earliest start: 2025-02-25 , Latest end: 2025-09-30

Organization Acoustic Robotics for Life Sciences and Healthcare (ARSL)

Hosts Medany Mahmoud

Topics Engineering and Technology , Physics

Loosely Guided Reinforcement Learning for Humanoid Parkour

Humanoid robots hold the promise of navigating complex, human-centric environments with agility and adaptability. However, training these robots to perform dynamic behaviors such as parkour—jumping, climbing, and traversing obstacles—remains a significant challenge due to the high-dimensional state and action spaces involved. Traditional Reinforcement Learning (RL) struggles in such settings, primarily due to sparse rewards and the extensive exploration needed for complex tasks. This project proposes a novel approach to address these challenges by incorporating loosely guided references into the RL process. Instead of relying solely on task-specific rewards or complex reward shaping, we introduce a simplified reference trajectory that serves as a guide during training. This trajectory, often limited to the robot's base movement, reduces the exploration burden without constraining the policy to strict tracking, allowing the emergence of diverse and adaptable behaviors. Reinforcement Learning has demonstrated remarkable success in training agents for tasks ranging from game playing to robotic manipulation. However, its application to high-dimensional, dynamic tasks like humanoid parkour is hindered by two primary challenges: Exploration Complexity: The vast state-action space of humanoids leads to slow convergence, often requiring millions of training steps. Reward Design: Sparse rewards make it difficult for the agent to discover meaningful behaviors, while dense rewards demand intricate and often brittle design efforts. By introducing a loosely guided reference—a simple trajectory representing the desired flow of the task—we aim to reduce the exploration space while maintaining the flexibility of RL. This approach bridges the gap between pure RL and demonstration-based methods, enabling the learning of complex maneuvers like climbing, jumping, and dynamic obstacle traversal without heavy reliance on reward engineering or exact demonstrations.

Keywords

humanoid, reinforcement learning, loosely guided

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Master Thesis

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**Work packages** Design a Loosely Guided RL Framework that integrates simple reference trajectories into the training loop. Evaluate Exploration Efficiency by comparing baseline RL methods with the guided approach. Demonstrate Complex Parkour Behaviors such as climbing, jumping, and dynamic traversal using the guided RL policy. Hardware validation encouraged **Requirements** Strong programming skills in Python Experience in reinforcement learning and imitation learning frameworks **Publication** This project will mostly focus on algorithm design and system integration. Promising results will be submitted to robotics or machine learning conferences where outstanding robotic performances are highlighted. **Related literature** Peng, Xue Bin, et al. "Deepmimic: Example-guided deep reinforcement learning of physics-based character skills." ACM Transactions On Graphics (TOG) 37.4 (2018): 1-14. Li, C., Vlastelica, M., Blaes, S., Frey, J., Grimminger, F. and Martius, G., 2023, March. Learning agile skills via adversarial imitation of rough partial demonstrations. In Conference on Robot Learning (pp. 342-352). PMLR. Li, Chenhao, et al. "FLD: Fourier latent dynamics for Structured Motion Representation and Learning." Serifi, A., Grandia, R., Knoop, E., Gross, M. and Bächer, M., 2024, December. Vmp: Versatile motion priors for robustly tracking motion on physical characters. In Computer Graphics Forum (Vol. 43, No. 8, p. e15175). Fu, Z., Zhao, Q., Wu, Q., Wetzstein, G. and Finn, C., 2024. Humanplus: Humanoid shadowing and imitation from humans. arXiv preprint arXiv:2406.10454. He, T., Luo, Z., Xiao, W., Zhang, C., Kitani, K., Liu, C. and Shi, G., 2024, October. Learning human-to-humanoid real-time whole-body teleoperation. In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 8944-8951). IEEE. He, T., Luo, Z., He, X., Xiao, W., Zhang, C., Zhang, W., Kitani, K., Liu, C. and Shi, G., 2024. Omnih2o: Universal and dexterous human-to-humanoid whole-body teleoperation and learning. arXiv preprint arXiv:2406.08858.

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Published since: 2025-02-25

Organization ETH Competence Center - ETH AI Center

Hosts Li Chenhao , Li Chenhao , Li Chenhao , Li Chenhao

Topics Information, Computing and Communication Sciences

Piezoelectric Atomization: Optimizing Liquid and Gel Dispersion

Piezoelectric elements are widely used for particle manipulation and atomization, with applications in humidification, cooling, and medical aerosol generation. However, temperature and environmental factors can impact the efficiency of vaporization and the properties of the generated droplets. Additionally, the heat generated by piezo elements affects particle size and flux, requiring careful control. This project will investigate the effect of piezoelectric elements on liquid and gel atomization, optimizing power consumption, repeatability, and calibration. A proof-of-concept demonstrator will be developed to study these parameters under controlled conditions. A successful outcome may lead to a subsequent R&D phase or PhD project in collaboration with Honeywell to further develop these findings

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Piezoelectric Atomization, Liquid/Gel Dispersion, Energy Efficiency, Particle Size Control, Low-Power Electronics

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Published since: 2025-02-25 , Earliest start: 2025-02-25 , Latest end: 2025-09-30

Organization Acoustic Robotics for Life Sciences and Healthcare (ARSL)

Hosts Medany Mahmoud

Topics Engineering and Technology , Physics

Learning World Models for Legged Locomotion

Model-based reinforcement learning learns a world model from which an optimal control policy can be extracted. Understanding and predicting the forward dynamics of legged systems is crucial for effective control and planning. Forward dynamics involve predicting the next state of the robot given its current state and the applied actions. While traditional physics-based models can provide a baseline understanding, they often struggle with the complexities and non-linearities inherent in real-world scenarios, particularly due to the varying contact patterns of the robot's feet with the ground. The project aims to develop and evaluate neural network-based models for predicting the dynamics of legged environments, focusing on accounting for varying contact patterns and non-linearities. This involves collecting and preprocessing data from various simulation environment experiments, designing neural network architectures that incorporate necessary structures, and exploring hybrid models that combine physics-based predictions with neural network corrections. The models will be trained and evaluated on prediction autoregressive accuracy, with an emphasis on robustness and generalization capabilities across different noise perturbations. By the end of the project, the goal is to achieve an accurate, robust, and generalizable predictive model for the forward dynamics of legged systems.

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forward dynamics, non-smooth dynamics, neural networks, model-based reinforcement learning

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Published since: 2025-02-25

Organization Robotic Systems Lab

Hosts Li Chenhao , Li Chenhao , Li Chenhao , Li Chenhao

Topics Engineering and Technology

Developing Multi-Functional Microrobots Using Microfluidic Chips (3M project)

We are looking for a motivated Master’s student to join an exciting interdisciplinary thesis project, collaborating between the Multi-Scale Robotics Lab (D-MAVT) and the deMello group (D-CHAB) at ETH Zurich. This project focuses on creating a novel microfluidic-based bottom-up method to fabricate multifunctional microrobots. This innovative approach seeks to revolutionize microrobot fabrication, opening the door to diverse new applications.

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Microfluidics, Self-assembly, Microrobots

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Master Thesis , ETH Zurich (ETHZ)

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Published since: 2025-02-21 , Earliest start: 2025-02-17

Organization Multiscale Robotics Lab

Hosts Hu Minghan

Topics Engineering and Technology , Chemistry

Refining FEM Models for Predicting Fretting Fatigue Occurrence in Dental Implants

This project investigates fretting fatigue in dental implants, focusing on the impact of small, repeated interfacial movements on fatigue life. By analyzing existing fatigue test data, performing SEM-based failure analysis, and refining computational models, the goal is to better predict fretting mechanisms and reduce the need for extensive experimental testing.

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Fretting Fatigue Dental Implants Fatigue Testing Computational Modeling SEM Analysis

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Published since: 2025-02-19 , Earliest start: 2025-04-01 , Latest end: 2025-07-01

Applications limited to ETH Zurich

Organization Experimental Continuum Mechanics

Hosts Hosseini Ehsan

Topics Engineering and Technology

Maturation Platform for Engineered 3D Muscle Tissues

The frontier of research in soft robotics aims at replacing classic soft materials used for actuators with biological ones (muscle) to take advantage of the innate adaptability, energy efficiency, and softness of biological systems. To grow more performant muscle tissues for robotics, we need to engineer a maturation platform for the muscle tissues that can provide the necessary mechanical and electrical cues. Therefore, in this project, we will aim to engineer a maturation platform for skeletal muscle bioactuators. The maturation platform will work as a device that applies cyclic stretch to the growing skeletal muscle tissue by pneumatic actuation. An initial prototype of the maturation platform has already been designed (see image). Your project will start from this prototype and do further optimization to result in a system that can give multiple stretching inputs to different muscle tissues.

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biohybrid robotics, soft robotics, 3D printing, biomaterials, actuation, material characterization

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Semester Project , Bachelor Thesis , Master Thesis , ETH Zurich (ETHZ)

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Published since: 2025-02-19 , Earliest start: 2025-02-20 , Latest end: 2025-10-31

Applications limited to EPFL - Ecole Polytechnique Fédérale de Lausanne , ETH Zurich , Empa , IBM Research Zurich Lab , University of Zurich

Organization Soft Robotics Lab

Hosts Balciunaite Aiste , Katzschmann Robert, Prof. Dr.

Topics Engineering and Technology

Design and Force-feedback Control of a Robotic Platform for Autonomous Eye Injections

The objective of this project is to advance an existing robotic platform capable of autonomously delivering injections into the human eye. Rather than starting from scratch, the student will build on significant prior research. The primary task is to implement a force-feedback control algorithm that acts as the system’s safety mechanism. Specifically, the student will develop an admittance control algorithm, enabling the robot to adapt to unexpected disturbances by using real-time data from a 6D eye-tracking device and a force sensor.

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feedback control, admittance control, industrial design

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Semester Project , Bachelor Thesis , Master Thesis

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Published since: 2025-02-19

Organization Multiscale Robotics Lab

Hosts Ehmke Claas

Topics Engineering and Technology

Design and Force-feedback Control of a Robotic Platform for Autonomous Eye Injections

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feedback control, compliance control, industrial design

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Published since: 2025-02-19 , Earliest start: 2025-02-20

Organization Multiscale Robotics Lab

Hosts Ehmke Claas

Topics Engineering and Technology

Granular Hydrogels for Chronic Wound Healing: Enhancing Stability, Transport, and Clinical Readiness

The development of biomaterials for chronic wound healing faces significant challenges in achieving shelf-stability, transportability, and compliance with clinical manufacturing standards. To address these hurdles, we aim to integrate a freeze-drying (lyophilization) step into the preparation of our granular hydrogels, facilitating storage and transport without compromising functionality. By validating the post-rehydration performance of lyophilized microgels, we aim to ensure the robustness of our product for clinical use.

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Published since: 2025-02-18 , Earliest start: 2025-04-01 , Latest end: 2025-12-31

Organization Macromolecular Engineering Laboratory

Hosts Emiroglu Börte

Topics Medical and Health Sciences , Chemistry , Biology

Ex vivo evaluation of wound healing using granular biomaterials

Chronic wound care is hindered by the complex and variable proteomic profiles of wound exudates, which limit the efficacy of existing therapies. We aim to validate the effectiveness of our granular hydrogel platform in restoring balance to the wound microenvironment. Utilizing exudates obtained from diabetic foot ulcer (DFU) patients, we will optimize our microgel library to target clinically relevant cytokine profiles.

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Published since: 2025-02-18 , Earliest start: 2025-03-01 , Latest end: 2025-12-31

Organization Macromolecular Engineering Laboratory

Hosts Emiroglu Börte , Singh Apoorv

Topics Medical and Health Sciences , Engineering and Technology , Chemistry , Biology

Development of Core@Shell Magnetoelectric Nanoparticles for Targeted Brain Stimulation

Magnetoelectric materials are highly promising in biomedicine with their unique ability to couple magnetic and electric fields. This coupling allows remote and precise control of various biological processes. For instance, in drug delivery, magnetoelectric nanoparticles can be directed to specific locations in the body using an external magnetic field, followed by electrical stimulation to trigger the release of therapeutic agents. The responsiveness and multifunctionality make magnetoelectric nanoparticles versatile tools in advancing non-invasive medical treatments and targeted therapies.

Keywords

Surface engineering, Nanoparticles, Iron oxide, Barium titanate, Brain Stimulation

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Semester Project , Bachelor Thesis , Master Thesis , ETH Zurich (ETHZ)

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Published since: 2025-02-18 , Earliest start: 2025-02-24 , Latest end: 2025-06-01

Applications limited to ETH Zurich

Organization Multiscale Robotics Lab

Hosts Pustovalov Vitaly

Topics Engineering and Technology , Chemistry

Solvothermal Synthesis of Metal Ferrite Nanoparticles for Small-Scale Robotic Applications

Metal ferrite nanoparticles have gathered attention due to their promising characteristics for biomedical applications, such as targeted drug delivery or diagnostics. Nanoparticle properties (e.g., morphology, size, magnetic response) play a critical role to enhance their efficacy during the application. On the other hand, reproducibility is still a considerable challenge in most of the synthesis approaches.

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Nanoparticles, Magnetism, Microrobotics, Iron oxide

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Published since: 2025-02-18 , Earliest start: 2025-02-24 , Latest end: 2025-06-01

Applications limited to ETH Zurich

Organization Multiscale Robotics Lab

Hosts Pustovalov Vitaly

Topics Engineering and Technology , Chemistry

Functional phenotyping of human brain organoids

Duplication 15q (Dup15q) syndrome is one of the most common forms of autism spectrum disorder (ASD). In Dup15q, the chromosome region 15q 11.2-13.1 is duplicated. Compared to other types of ASDs, it has been reported that individuals suffering from Dup15q show significantly more impairments in regard to motor- and daily living skills. Many Dup15q patients also have epilepsy, which is correlated to altered cognitive and adaptive functions. Recent studies using whole-cell patch clamp and calcium imaging techniques have demonstrated that Dup15 human neurons demonstrate significant differences in some electrophysiological parameters, indicating a hyperexcitability phenotype. These alterations were mainly attributed to overexpression of the protein UBE3A, but the exact mechanisms leading to this phenotype are not yet fully understood. In this project, we will use cerebral organoids generated from Dup15q patient derived induced pluripotent stem cell (iPSC) lines, and isogenic controls, to electrophysiologically characterize and model Dup15q syndrome in vitro. This project will also entail analysis of the electrophysiological data using computational methods such as spike sorting and functional connectivity analysis.

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Brain organoids, neurodevelopmental disorders, high-density microelectrode arrays

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Published since: 2025-02-17 , Earliest start: 2025-04-01 , Latest end: 2025-12-31

Organization Bio Engineering Laboratory

Hosts Schröter Manuel

Topics Medical and Health Sciences

Can we consider spore as ellipsoid polystyrene particles? – Characterization of ellipsoid polystyrene particles

This research focuses on understanding the colloidal behaviour of bacterial spores through diverse characterization techniques. Your work will focus on characterizing ellipsoid polystyrene particles instead of Bacillus subtilis spores. The same characterization experiments had already been conducted on Bacillus subtilis spores, providing valuable experience with the required procedures.

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Particle characterization, particle at interface

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Semester Project , Internship , Bachelor Thesis , Master Thesis , Summer School

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Published since: 2025-02-14 , Earliest start: 2025-03-02

Organization Interfaces, Soft matter and Assembly

Hosts Hu Yiyao

Topics Engineering and Technology

Jellyfish Robot Design and Acoustically Assisted Motion Control Using AI

Jellyfish-inspired robots have gained significant attention in soft robotics and biomimetic engineering due to their energy efficiency, silent propulsion, and adaptability to aquatic environments. The AI-powered Jellyfish robots offer a promising avenue for developing next-generation robotic systems with applications in biomedical research, environmental monitoring, and marine life interaction.

Keywords

AI-powered soft robotics, Jellyfish-inspired locomotion, Acoustically assisted propulsion, Machine learning-based motion control.

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Master Thesis , ETH Zurich (ETHZ)

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Published since: 2025-02-14 , Earliest start: 2025-02-17 , Latest end: 2025-07-31

Organization Acoustic Robotics for Life Sciences and Healthcare (ARSL)

Hosts Zhong Chengxi

Topics Engineering and Technology

Ultrasound Transducer Array based Helmet for Transcranial Therapy

Ultrasound-based transcranial therapy is emerging as a non-invasive and highly precise technique for treating neurological disorders, enhancing drug delivery, and promoting brain stimulation. By leveraging an advanced ultrasound transducer array embedded in a wearable helmet, this project aims to develop a novel system for targeted, real-time brain therapy

Keywords

Transcranial ultrasound therapy, Steering ultrasound field, Ultrasound transducer array design.

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Master Thesis , ETH Zurich (ETHZ)

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Published since: 2025-02-14 , Earliest start: 2025-02-17 , Latest end: 2025-07-31

Organization Acoustic Robotics for Life Sciences and Healthcare (ARSL)

Hosts Zhong Chengxi

Topics Medical and Health Sciences , Engineering and Technology

Development of a linear electrostatic film actuator for a Humanoid Robotic Hand

Development of a linear electrostatic film actuator for soft robotic applications such as the actuation of a humanoid robotic hand.

Keywords

Electrostatic, Linear actuator, Flexible electronics, Soft Robotics, Humanoid Robotic Hands

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Published since: 2025-02-13 , Earliest start: 2025-02-23 , Latest end: 2025-12-31

Organization Soft Robotics Lab

Hosts Katzschmann Robert, Prof. Dr.

Topics Engineering and Technology

Design and Fabrication of Dexterous, Humanoid Robotic Hand

Design and build dexterous human-like robotic hands with us at the Soft Robotics Lab and the spin-off mimic. We will explore different possibilities of developing design features such as tendon-driven mechanisms, lightweight structures, and complex mechanical joints of the hand. The developed features shall be integrated into a fully functional robotic hand and applied to solve practical manipulation challenges.

Keywords

humanoid, robotics, hand, dexterity, soft robotics, actuation, prototyping, modeling and control, mechatronics, biomimetic, design, 3D printing, silicone casting

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Published since: 2025-02-13 , Earliest start: 2024-09-01 , Latest end: 2025-07-24

Organization Soft Robotics Lab

Hosts Hinchet Ronan , Katzschmann Robert, Prof. Dr. , Weirich Stefan

Topics Engineering and Technology

FPGA-based platform for in-vitro neural interfaces

The student will develop an FPGA-based platform for interacting with in-vitro neuronal cultures, including real-time spike detection and neuron stimulation. The project is focused on VHDL programming, although it could include other software development and/or wet-lab tasks with neurons depending on the interests of the student.

Keywords

VHDL; FPGA; Engineering; Neural interface; In-vitro neuronal culture; CMOS Microelectrode Array; Neuroscience.

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Semester Project , Internship , Bachelor Thesis , Master Thesis

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Published since: 2025-02-12 , Earliest start: 2022-09-01

Organization Bio Engineering Laboratory

Hosts Cardes Fernando

Topics Medical and Health Sciences , Engineering and Technology

Living Composite Material

Microorganisms that can be found everywhere in our environment are able to produce a variety of molecules from very simple precursors. Some of the products synthetized by bacteria are materials with fascinating properties such as cellulose with excellent mechanical properties. Materials produced by living cells are attractive because they are produced with minimal energy input and are based on green chemicals. Moreover, if well-designed, these materials still contain the living cells and are thus able to react onto external stimuli. Therefore, they have the potential to repair themselves upon damage or form materials with locally defined microstructures and architectures.

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Living Materials, Bacteria, Material Science, Master Thesis

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Semester Project , Internship , Bachelor Thesis , Master Thesis , Master Project (D-MATL)

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Published since: 2025-02-12 , Earliest start: 2024-01-08

Organization Complex Materials

Hosts Steinacher Mathias, Mr.

Topics Engineering and Technology , Chemistry , Biology

Supervised learning for loco-manipulation

To spot arm operations, we propose a multi-phase approach combining supervised learning and reinforcement learning (RL). First, we will employ supervised learning to develop a model for solving inverse kinematics (IK), enabling precise joint angle calculations from desired end-effector pose. Next, we will utilize another supervised learning technique to build a collision avoidance model, trained to predict and avoid self-collisions based on arm configurations and environmental data. With these pre-trained networks, we will then integrate RL to generate dynamic and safe arm-motion plans. The RL agent will leverage the IK and collision avoidance models to optimize arm trajectories, ensuring efficient and collision-free movements. This entire pipeline could be back propagated while promising to enhance the accuracy, safety, and flexibility of robotic arm operations in complex environments.

Keywords

Spot, Supervised learning, loco-manipulation

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Master Thesis , ETH Zurich (ETHZ)

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Published since: 2025-02-10 , Earliest start: 2025-02-10 , Latest end: 2026-03-01

Organization Robotic Systems Lab

Hosts Mirrazavi Sina

Topics Information, Computing and Communication Sciences

Model-Based Reinforcement Learning for Loco-manipulation

This project aims to develop a model-based reinforcement learning (RL) framework to enable quadruped robots to perform dynamic locomotion and manipulation simultaneously by leveraging advanced model-based RL algorithms such as DeamerV3, TDMPC2 and SAM-RL. We will develop control policies that can predict future states and rewards, enabling the robot to adapt its behavior on-the-fly. The primary focus will be on achieving stable and adaptive walking patterns while reaching and grasping objects. The outcome will provide insights into the integration of complex behaviors in robotic systems, with potential applications in service robotics and automated object handling.

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Master Thesis , ETH Zurich (ETHZ)

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Published since: 2025-02-10 , Earliest start: 2025-02-10 , Latest end: 2026-02-10

Organization Robotic Systems Lab

Hosts Mirrazavi Sina

Topics Information, Computing and Communication Sciences

Integrating OpenVLA for Vision-Language-Driven Loco-Manipulation robotics scenarios

This thesis proposes to integrate and adapt the OpenVLA (Open-Source Vision-Language-Action) model to control the Spot robotic arm for performing complex grasping and placing tasks. The study will focus on enabling the robot to recognize, grasp, and organize various toy-sized kitchen items based on human instructions. By leveraging OpenVLA's robust multimodal capabilities, this project aims to bridge the gap between human intent and robotic actions, enabling seamless task execution in unstructured environments. The research will explore the feasibility of fine-tuning OpenVLA for task-specific operations and evaluate its performance in real-world scenarios, providing valuable insights for advancing multimodal robotics.

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Master Thesis , ETH Zurich (ETHZ)

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Published since: 2025-02-10 , Earliest start: 2025-02-10 , Latest end: 2026-02-10

Organization Robotic Systems Lab

Hosts Mirrazavi Sina

Topics Information, Computing and Communication Sciences

Differentiable Simulation for Precise End-Effector Tracking

Unlock the potential of differentiable simulation on ALMA, a quadrupedal robot equipped with a robotic arm. Differentiable simulation enables precise gradient-based optimization, promising greater tracking accuracy and efficiency compared to standard reinforcement learning approaches. This project dives into advanced simulation and control techniques, paving the way for improvements in robotic trajectory tracking.

Keywords

Differentiable Simulation, Learning, ALMA

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Semester Project , Bachelor Thesis , Master Thesis

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Published since: 2025-02-07 , Earliest start: 2025-01-27

Organization Robotic Systems Lab

Hosts Mittal Mayank , Schwarke Clemens , Klemm Victor

Topics Information, Computing and Communication Sciences

Modeling and Simulation for Earthwork in Digital Twin

In this work, we aim to build a digital twin of our autonomous hydraulic excavator, leveraging Mathworks technology for high-fidelity modeling. This will be used in the future to test and benchmark our learning-based controllers.

Keywords

Modeling, Hydraulics, Excavation, Industry

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Semester Project , Master Thesis , ETH Zurich (ETHZ)

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Published since: 2025-02-06 , Earliest start: 2025-03-03

Organization Robotic Systems Lab

Hosts Spinelli Filippo , Nan Fang

Topics Information, Computing and Communication Sciences , Engineering and Technology

Untethered ultrafast-rotating spiral microrobot for physical thrombolysis

The ability to manipulate micro-scale objects with precision is a growing field in biomedical engineering, particularly in the context of treating thrombotic conditions. Thrombolysis, the process of dissolving blood clots, remains a significant challenge in medical treatment, with current techniques often limited by their invasiveness and effectiveness. Recent advancements have explored the use of microrobots for targeted thrombolysis, leveraging their ability to maneuver in complex biological environments to enhance clot dissolution and drug delivery. Rotation plays a crucial role in various natural processes, including feeding and locomotion, demonstrating its effectiveness in achieving complex interactions with the environment. However, achieving ultrafast rotation in artificial microrobots presents significant engineering challenges. Traditional methods of inducing rotation, such as acoustic manipulation, have shown promise but are often constrained by limitations in rotational speed and control precision. These constraints hinder the microrobot's ability to effectively engage with functions. In response to these challenges, we introduce an innovative solution: an untethered ultrafast-rotating spiral microrobot designed for physical thrombolysis. This microrobot employs a symmetric spiral structure that generates a consistent torque while maintaining a zero net force, allowing for sustained high-speed rotation. The unique design of the spiral structure ensures efficient rotational motion, overcoming previous limitations in rotation speed. A key feature of our microrobot is its sharp-tip design, which enhances its ability to penetrate and mechanically disrupt thrombi. This mechanical drilling action facilitates the breakdown of clots, making thrombolysis more effective. Additionally, the microrobot incorporates a drug-holding cavity, enabling it to deliver therapeutic agents directly to the site of the thrombus. This dual functionality—mechanical disruption combined with targeted drug delivery—promises a more efficient approach to thrombolysis. This ultrafast-rotating microrobot represents a significant advancement in microrobot design and its application in medical treatments.

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Keywords: Rotation; acoustic microrobot; thrombolysis

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Master Thesis

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Published since: 2025-02-05 , Earliest start: 2025-02-06 , Latest end: 2025-09-30

Organization Acoustic Robotics for Life Sciences and Healthcare (ARSL)

Hosts Deng Yong

Topics Engineering and Technology

Designing Freeform Trajectories through Acoustic Streaming and Artificial Intelligence

The manipulation of materials and fluids through acoustic streaming has emerged as a powerful technique with applications in manufacturing and biomedical engineering. This method utilizes sound waves to control the movement of particles within a fluid, offering precise and non-invasive manipulation. However, achieving freeform path manipulation—guiding materials along complex, non-linear trajectories—remains a significant challenge due to difficulties in controlling the influence range and vortex dynamics of acoustic streaming. Traditional methods often struggle with maintaining precision and stability along intricate paths, as the non-uniform distribution of acoustic forces complicates consistent directionality. Artificial Intelligence (AI) presents a promising solution, enabling real-time control and optimization of these systems. By integrating AI with acoustic streaming, algorithms can analyze and predict the interactions between acoustic forces and fluid dynamics, allowing for dynamic adjustments that enhance accuracy. In this thesis, we propose addressing these challenges by implementing a pillar array of acoustic actuators coupled with AI-driven control systems. The pillar array will generate and modulate acoustic streaming fields, while AI will optimize and automate their control in real time. This integration aims to improve the precision of freeform path manipulation, facilitating the creation of complex patterns that are otherwise difficult to achieve, thereby expanding the possibilities for material manipulation across various applications.

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Freeform path; Manipulation; Ultrasound; pillar array; AI

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Semester Project , Bachelor Thesis , Master Thesis

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Published since: 2025-02-05 , Earliest start: 2025-02-06 , Latest end: 2025-09-30

Organization Acoustic Robotics for Life Sciences and Healthcare (ARSL)

Hosts Deng Yong

Topics Engineering and Technology

Computational Modeling of Muscle Dynamics for Biohybrid Robots

This research aims to advance biohybrid robotics by integrating living biological components with artificial materials. The focus is on developing computational models for artificial muscle cells, a critical element in creating biohybrid robots. Challenges include modeling the complex and nonlinear nature of biological muscles, considering factors like elasticity and muscle fatigue, as well as accounting for fluid-structure interaction in the artificial muscle's environment. The research combines first principle soft body simulation methods and machine learning to improve understanding and control of biohybrid systems.

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Biohybrid Robotics, Computational Models, Soft Body Simulation, Finite Element Method (FEM), Muscle Dynamics, Soft Robotics

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Semester Project , Bachelor Thesis , Master Thesis

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Published since: 2025-02-04 , Earliest start: 2025-02-01 , Latest end: 2026-02-01

Organization Soft Robotics Lab

Hosts Mekkattu Manuel , Katzschmann Robert, Prof. Dr.

Topics Mathematical Sciences , Information, Computing and Communication Sciences , Engineering and Technology , Biology , Physics

GPU Acceleration of Soft Robot Modeling: Enhancing Performance with CUDA

We are enhancing soft robot modeling by developing a GPU-accelerated version of our FEM-based framework using CUDA. This research focuses on optimizing parallel computations to significantly speed up simulations, enabling larger problem sizes and real-time control. By improving computational efficiency, we aim to advance soft robotics research and facilitate more detailed, dynamic simulations.

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Soft Body Simulation, high-performance computing, GPU programming, Parallel Computing, Finite Element Method (FEM), Multiphysics Simulation

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Semester Project , Bachelor Thesis , Master Thesis

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Published since: 2025-02-04 , Earliest start: 2025-02-01 , Latest end: 2026-02-01

Organization Soft Robotics Lab

Hosts Katzschmann Robert, Prof. Dr. , Mekkattu Manuel

Topics Information, Computing and Communication Sciences , Engineering and Technology

Advancing Soft Robot Modeling: Integrating Physics, Optimization, and Control

We are advancing soft robot simulation with FEM and energy-based methods to model complex, adaptive behaviors. This research entails developing the framework to support diverse designs, integrate new physics models, and optimize performance, enabling enhanced control and real-world applications of soft robots.

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Soft Robotics, Finite Element Method (FEM), Physical Modeling, Benchmarking, Optimization, Multiphysics Simulation, Sim-to-Real

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Semester Project , Bachelor Thesis , Master Thesis

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Published since: 2025-02-04 , Earliest start: 2025-02-01 , Latest end: 2026-02-01

Organization Soft Robotics Lab

Hosts Mekkattu Manuel , Katzschmann Robert, Prof. Dr.

Topics Information, Computing and Communication Sciences , Engineering and Technology

Reinforcement Learning for Excavation Planning In Terra

We aim to develop a reinforcement learning-based global excavation planner that can plan for the long term and execute a wide range of excavation geometries. The system will be deployed on our legged excavator.

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Keywords: Reinforcement learning, task planning

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Semester Project , Master Thesis

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Published since: 2025-02-03 , Earliest start: 2025-04-01 , Latest end: 2025-08-31

Organization Robotic Systems Lab

Hosts Terenzi Lorenzo

Topics Information, Computing and Communication Sciences

Model Based Reinforcement Learning

We want to train an excavator agent to learn in a variety of soil using a fast, GPU-accelerated soil particle simulator in Isaac Sim.

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particle simulation, omniverse, warp, reinforcement learning, model based reinforcement learning.

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Semester Project , Master Thesis

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Published since: 2025-02-03 , Earliest start: 2025-02-28 , Latest end: 2025-08-31

Organization Robotic Systems Lab

Hosts Egli Pascal Arturo , Terenzi Lorenzo

Topics Information, Computing and Communication Sciences , Engineering and Technology

Reinforcement Learning for Particle-Based Excavation in Isaac Sim

We want to train RL agents on our new particle simulator, accelerated on the GPU via warp in Isaac sim.

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particle simulation, omniverse, warp, reinforcement learning

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Semester Project , Master Thesis

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Published since: 2025-02-03 , Earliest start: 2025-04-01 , Latest end: 2025-09-30

Organization Robotic Systems Lab

Hosts Egli Pascal Arturo , Mittal Mayank , Terenzi Lorenzo

Topics Information, Computing and Communication Sciences

Perceptive Reinforcement Learning for Exavation

In this project, our goal is to leverage precomputed embeddings(VAE in Isaacsim) from 3D earthworks scene reconstructions to train reinforcement learning agents. These embeddings, derived from incomplete point cloud data and reconstructed using an encoder-decoder neural network, will serve as latent representations. The main emphasis is on utilizing these representations to develop and train reinforcement learning policies for digging tasks.

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LIDAR, 3D reconstruction, Isaac gym, deep learning, perception, reinforcement learning

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Semester Project , Master Thesis

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Published since: 2025-02-03 , Earliest start: 2025-04-01 , Latest end: 2025-08-31

Organization Robotic Systems Lab

Hosts Höller David , Terenzi Lorenzo

Topics Information, Computing and Communication Sciences

Reiforcement Learning of Pretrained Trasformer Models

We want to train RL agents on our new particle simulator, accelerated on the GPU via warp in Isaac sim.

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Keywords: particle simulation, omniverse, warp, reinforcement learning

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Semester Project , Master Thesis

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Published since: 2025-02-03 , Earliest start: 2025-04-01 , Latest end: 2025-08-31

Organization Robotic Systems Lab

Hosts Terenzi Lorenzo

Topics Information, Computing and Communication Sciences

Multiagent Reinforcement Learning in Terra

We want to train multiple agents in the Terra environment, a fully end-to-end GPU-accelerated environment for RL training.

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multiagent reinforcement learning, jax, deep learning, planning

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Semester Project , Master Thesis

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Published since: 2025-02-03 , Earliest start: 2025-04-01 , Latest end: 2025-09-16

Organization Robotic Systems Lab

Hosts Terenzi Lorenzo

Topics Information, Computing and Communication Sciences

Synthesis and characterization of Na-Fe-Mn-Ni-oxide cathode materials for sodium-ion batteries

Sodium-ion batteries (SIBs) present a promising alternative to lithium-ion batteries, attracting significant interest due to their low cost and the abundance of sodium resources. Among the key cathode materials for SIBs are sodium transition metal oxides containing Ni, Mn, and Fe. Depending on their metal composition and defect concentration, these oxides exhibit different crystallographic phases, leading to variations in their material and electrochemical properties. This master thesis is part of a broader project aimed at investigating the material and electrochemical properties of a wide range of sodium-based cathode materials. Building on our recent research on lithium-ion battery cathode materials, the primary objective of this thesis is to synthesize and characterize selected cathode materials within the Na-Fe-Mn-Ni oxide system and identify property trends. Through this project, the student will gain hands-on experience in cathode material synthesis and characterization techniques, including X-ray diffraction (XRD) and scanning electron microscopy (SEM), as well as in cell assembly and battery testing. Students with a background in materials science or chemistry are encouraged to apply.

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sodium ion batteries, cathode materials, electrochemistry, energy storage

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Master Thesis

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Published since: 2025-01-31 , Earliest start: 2025-03-03 , Latest end: 2025-12-31

Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne

Organization Materials and Device Engineering Group (Wood)

Hosts Shunmugasundaram Ramesh

Topics Engineering and Technology , Chemistry

Propose Your Own Robotics Challenge

This project invites you to step into the role of an innovator, encouraging you to identify challenges you are passionate about within the field of robotics. Rather than working on predefined problems, you will have the freedom to propose your own project ideas, address real-world issues, or explore cutting-edge topics. This project allows you to define your own research journey.

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Robotics, Research

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Semester Project , Bachelor Thesis , Master Thesis

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Published since: 2025-01-28 , Earliest start: 2025-01-27

Organization Robotic Systems Lab

Hosts Schwarke Clemens , Bjelonic Filip , Klemm Victor

Topics Information, Computing and Communication Sciences

Inline Quality Control with image analysis & AI

diaxxo, a start-up from ETH Zürich, is revolutionizing molecular diagnostics with a cutting-edge Point-of-Care PCR device. Their innovative technology facilitates rapid, accurate diagnostic testing across human, veterinary, and food applications, especially in developing countries. The PCR process amplifies DNA sequences to identify pathogens accurately. Key to diaxxo's system are specialized aluminum cartridges containing pre-loaded, dried reagents, essential for precise diagnostics. However, current manufacturing challenges in reagent loading and drying affect cartridge quality. The project aims to develop a Quality Control station using advanced imaging and AI to ensure accurate reagent placement and drying, enhancing diagnostic reliability and effectiveness.

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Image Analysis, AI Technology, High-Resolution Imaging, Visual Data, Quality Control (QC), Automated Decision Making, Defect Detection, Machine Learning, pattern Recognition, Real-Time Analysis

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Semester Project , Bachelor Thesis , Master Thesis

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Context diaxxo, a start-up from ETH Zürich, is transforming molecular diagnostics with an innovative Point-of-Care Polymerase Chain Reaction (PCR) device. Designed to accelerate and democratize access to diagnostic testing, our cutting-edge technology can be used across various fields, from human diagnostics to vet and food testing. Our products are also tailored for use in developing countries and resource-limited settings, aiming to bring reliable diagnostics to every corner of the globe. Project Background Polymerase Chain Reaction (PCR) diagnostics is a powerful technique used to amplify and detect specific DNA sequences, enabling precise identification of pathogens and genetic conditions. At a high level, PCR works by repeatedly heating and cooling DNA samples in a process called thermocycling. This causes the DNA to denature, anneal with primers, and extend, exponentially amplifying the target sequence. During the test, the entire reaction takes place within a specialized cartridge that contains all the necessary reagents. This cartridge is a critical component, designed to facilitate the precise conditions required for DNA amplification. In diaxxo's system, the cartridge features a unique design made from aluminum. The PCR cartridges contain all the necessary reagents pre-loaded and dried. This pre-loading process ensures that the reagents are readily available for the PCR reaction, facilitating rapid and accurate testing. Ensuring the correct loading and drying of these reagents is crucially important. Proper loading guarantees that each cartridge has the precise amount of each reagent needed for the PCR process, while adequate drying prevents degradation and ensures the reagents remain stable until use. Both steps are critical to achieving reliable and consistent diagnostic results. Current issues diaxxo currently operates a manufacturing line that involves the spotting and drying of reagents within the PCR cartridges. However, there are several critical issues that need to be addressed to ensure the production of high-quality cartridges: 1. Correct Loading: It is essential to verify that each cartridge is loaded with the precise amount of reagents. Inaccurate loading can lead to suboptimal PCR reactions, affecting the reliability and accuracy of diagnostic results. 2. Proper Drying: The drying process must be carefully controlled to ensure that reagents remain stable and effective until use. Inadequate drying can result in reagent degradation or variability in reaction conditions. Ensuring both correct loading and proper drying is crucial to prevent the production of defective cartridges, which can compromise the overall effectiveness of the PCR diagnostics and lead to unreliable results.

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Published since: 2025-01-27 , Earliest start: 2024-07-21 , Latest end: 2025-06-30

Organization Functional Materials Laboratory

Hosts Gregorini Michele

Topics Information, Computing and Communication Sciences , Engineering and Technology

Designing photosynthetic living materials with synthetic biology

Living materials, as an emerging field that combines biology and material science, are materials composed of immobilized living organisms and a carrier matrix providing pre-determined bio-functionality. [1,2] Living materials bring about new properties that are not easily realised by conventional materials. Here, we aim to design a new type of living materials that can sequester and store atmospheric CO2 irreversibly in the form of calcium carbonate minerals.

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living materials, synthetic biology, microorganisms

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Bachelor Thesis , Master Thesis , ETH Zurich (ETHZ)

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Published since: 2025-01-27 , Earliest start: 2025-02-05 , Latest end: 2025-10-31

Organization Macromolecular Engineering Laboratory

Hosts Cui Yifan

Topics Engineering and Technology

Data Driven Simulation for End-to-End Navigation

Investigate how neural rendering can become the backbone of comprehensive, next generation data-driven simulation

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Neural rendering, Simulation

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Internship , Master Thesis

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Published since: 2025-01-24 , Earliest start: 2025-01-27

Organization Robotic Systems Lab

Hosts Kneip Laurent

Topics Information, Computing and Communication Sciences , Engineering and Technology

Evolving Minds: Neuroevolution for Legged Locomotion

This project explores the use of neuroevolution for optimizing control policies in legged robots, moving away from classical gradient-based methods like PPO. Neuroevolution directly optimizes network parameters and structures, potentially offering advantages in environments with sparse rewards, while requiring fewer hyperparameters to tune. By leveraging genetic algorithms and evolutionary strategies, the project aims to develop efficient controllers for complex locomotion tasks. With computational capabilities doubling approximately every two years as predicted by Moore's Law, neuroevolution offers a promising approach for scaling intelligent control systems.

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Evolutionary Algorithms, Reinforcement Learning, Quadrupeds, Legged Locomotion

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Master Thesis

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Published since: 2025-01-22 , Earliest start: 2025-01-27

Organization Robotic Systems Lab

Hosts Bjelonic Filip , Schwarke Clemens

Topics Information, Computing and Communication Sciences

Development of instrumentation and analytics for powder bed quality for additive manufacturing

The quality of powder bed-based additive manufacturing processes is heavily dependent on the uniformity and repeatability of the powder spreading process. Left undetected, deviations in the powder bed can result in defective parts, which may lead to catastrophic failures. This project is to develop instrumentation and signal processing algorithms that can detect and classify anomalies in the powder bed of a metal 3D printer based on image recognition, machine learning, and/or artificial intelligence.

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Mechanical design, instrumentation, signal processing, image processing, statistics, additive manufacturing, 3D printing, quality control, machine learning, artificial intelligence, image recognition, neural network

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Published since: 2025-01-20 , Earliest start: 2023-10-23 , Latest end: 2024-10-22

Organization Institute of Machine Tools and Manufacturing

Hosts Tucker Michael Robert

Topics Engineering and Technology

Design and fabrication of thermally tunable metallic structures

Certain design applications require structures that remain dimensionally stable despite large swings in ambient temperature. Typically, these make use of exotic materials that are highly toxic and challenging to machine. In this project, we will investigate the producibility of dimensionally stable metallic structures that are produced from conventional materials via a novel additive manufacturing (i.e. 3D printing) process.

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Mechanical engineering, materials engineering, finite element modeling, structural design and analysis, 3D printing, additive manufacturing

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Published since: 2025-01-20 , Earliest start: 2022-10-05 , Latest end: 2023-10-04

Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne , CSEM - Centre Suisse d'Electronique et Microtechnique , Empa

Organization Institute of Machine Tools and Manufacturing

Hosts Tucker Michael Robert

Topics Engineering and Technology

Control Software Development for Advanced Laser Powder Bed Fusion Additive Manufacturing

In this project, software will be written to enable advanced multi-laser and beam shaping process control for deployment to a commercial laser powder bed fusion additive manufacturing machine.

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Mechanical engineering, electrical and control systems, software development, 3D printing, additive manufacturing

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Published since: 2025-01-20 , Earliest start: 2022-10-05 , Latest end: 2023-10-04

Organization Advanced Manufacturing Laboratory

Hosts Tucker Michael Robert

Topics Engineering and Technology