Student Projects

Currently, the following student projects are available. Please contact the responsible supervisor and apply with your CV and transcripts.

If you are looking for a Master internship HST/BWS or would like to carry out a Studies on Mechatronics project in our group, please contact the assistant working on, or closest to, the research topic you are interested in.

Instrumentation of ice hockey skates to determine on ice performance

This semester or master thesis aims to instrument ice hockey skates to enable direct measurement of three-dimensional forces exerted by competitive ice hockey players on the ice during training and test scenarios.

Keywords

sports engineering, portable force transducer, ice hockey, skate instrumentation

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Semester Project , Master Thesis

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Published since: 2025-10-17 , Earliest start: 2026-01-05 , Latest end: 2026-09-15

Applications limited to ETH Zurich

Organization Sensory-Motor Systems Lab

Hosts Wolf Peter

Topics Engineering and Technology

Smart Breathing for Stress Reduction – A Closed-Loop Mobile Application

This master thesis aims to develop a smartphone-based system for stress monitoring and reduction. The App, implemented in React Native (JavaScript) or another suitable framework, will use stress-related physiological parameters to deliver audio feedback that guides controlled breathing in real time.

Keywords

stress monitoring, breathing rate, App programming, mobile health, biofeedback

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Master Thesis

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Published since: 2025-10-02 , Earliest start: 2025-11-03 , Latest end: 2026-06-01

Organization Sensory-Motor Systems Lab

Hosts Padilla Neira Sara

Topics Medical and Health Sciences , Engineering and Technology

Causal Machine Learning and Data Fusion with Experts in the Loop for Spinal Cord Injury (SCI)

Causl Discovery aims to find causal relations from data, being increasingly important in various fields such as health science. Despite the growing amount of work on applying causal discovery methods with expert knowledge to areas of interest, few of them inspect the uncertainty of expert knowledge (what if the expert goes wrong?). This is highly important since in scientific fields, causal discovery with expert knowledge should be cautious and an approach taking expert uncertainty into account will be more robust to potential bias induced by individuals. Therefore, we aim to develop an iterative causal discovery method with experts in the loop to enable continual interaction and calibration between experts and data. Besides, fusing datasets from different sources is essential for holistic discovery and reasoning. This project will also focus on developing methods of machine learning and data fusion over distinct contexts under the scope of SCI. Based on the qualifications of the candidates, we can arrange a subsidy/allowance to cover traveling or living costs.

Keywords

Causal Discovery, Expert Knowledge, Iterative Algorithm, Data Fusion, Spinal Cord Injury

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Master Thesis

Project Background

Causal graphical models (CGM) can be determined via randomized controlled experiments (and this is the golden standard to determine the causal relations and quantify the causal effects), learned purely from data[1](i.e., structural learning or causal discovery) or elicited by domain experts based on their knowledge and experiences. It is also feasible to learn the CGM from data with prior expert knowledge when data become not reliable due to insufficient sample size or measurement errors. Early works in causal discovery utilized constraint-based approaches, such as the PC algorithm and the FCI algorithm. These methods exploit conditional independence relationships among variables to infer causal structures. They can restrict the output graph space through conditional independence assumptions or background knowledge about causal relationships [2]. Score-based approaches, such as the GES (Greedy Equivalence Search) algorithm, aim to find the best causal structure that optimizes a scoring criterion, e.g., the BIC (Bayesian Information Criterion) score. These methods often incorporate expert knowledge through prior specifications or domain-specific constraints on the model parameters [3, 4, 5]. Recent advancements in causal discovery involve hybrid approaches that combine elements of constraint-based and score-based methods. These approaches aim to leverage the strengths of both approaches to improve the accuracy and efficiency of causal structure learning. They may incorporate expert knowledge through the integration of domain-specific constraints and scoring functions. In addition to observational data, causal discovery methods have also been developed to handle experimental data, where interventions or randomized experiments are conducted. These methods utilize the causal effects of interventions to infer causal relationships. Expert knowledge can be incorporated by designing well-controlled experiments or interventions based on domain expertise. Despite the growing amount of work on structural learning with expert knowledge in scientific fields, few of them inspect the uncertainty of expert knowledge (what if the expert goes wrong?). This is highly important since in medical applications, causal discovery with expert knowledge should be taken cautiously and sometimes experts can give false knowledge, leading to serious mistakes. An approach considering the expert uncertainty during CGM construction will be more robust to potential bias induced by individuals. This idea is made possible by recent progress toward evaluating and falsifying a given CGM from observational data without ground truth[6]. Therefore, we hope to develop an iterative causal discovery method with experts in the loop for continual interaction and calibration between experts and data. In the meantime, we are also trying to develop an interactive user interface for medical experts to input their knowledge and edit the learned graph. References [1] Glymour, C., Zhang, K., & Spirtes, P. (2019). Review of causal discovery methods based on graphical models. Frontiers in genetics, 10, 524. [2] Flores, M. J., Nicholson, A. E., Brunskill, A., Korb, K. B., & Mascaro, S. (2011). Incorporating expert knowledge when learning Bayesian network structure: a medical case study. Artificial intelligence in medicine, 53(3), 181-204. [3] O’Donnell, R. T., Nicholson, A. E., Han, B., Korb, K. B., Alam, M. J., & Hope, L. R. (2006). Causal discovery with prior information. In AI 2006: Advances in Artificial Intelligence: 19th Australian Joint Conference on Artificial Intelligence, Hobart, Australia, December 4-8, 2006. Proceedings 19 (pp. 1162-1167). Springer Berlin Heidelberg. [4] Perković, E., Kalisch, M., & Maathuis, M. H. (2017). Interpreting and using CPDAGs with background knowledge. arXiv preprint arXiv:1707.02171. [5] Kleinegesse, S., Lawrence, A. R., & Chockler, H. (2022). Domain Knowledge in A*-Based Causal Discovery. arXiv preprint arXiv:2208.08247. [6] Eulig, E., Mastakouri, A. A., Blöbaum, P., Hardt, M., & Janzing, D. (2023). Toward Falsifying Causal Graphs Using a Permutation-Based Test. arXiv preprint arXiv:2305.09565.

Your Task

1. Literature Review on Causal Discovery: You will study classic and state-of-the-art causal discovery methods (preferably on real datasets with mixed-type variables) that incorporate expert knowledge, leading to a systematic review of causal discovery methods in different categories (constraint-based vs. score-based) with different assumptions (e.g., latent variables, causal mechanism/function forms, and cycles) for different settings (e.g., static data or time series). 2. Data Exploration and Analysis: You will explore analyze and datasets collected for HAPI (Hospital-Acquired Pressure Injury) and Hospital Stay after PI Surgeries. The data record hundreds of patients into dozens of mixed-type variables including patients’ demographic features, lab testing values, and health conditions observed during their stay. Apart from clinical data, you will also have access to multivariate time series extracted from bio-signals on SCI people under intervention experiments. We aim to learn the causal relations among these variables of interest from the above data with experts’ knowledge. Additionally, we will probe some public datasets of other fields such as heart diseases and earth science with ground-truth graphs. 3. Methodology Development and Deployment: You will help develop and validate an iterative algorithm for causal discovery with the expert in the loop against benchmarks using the simulated and real datasets. There are two potential exploring directions: 1. How will the correct expert knowledge help reduce the search space of graphs to improve the accuracy and efficiency of the algorithm? 2. How to encode prior knowledge or design a post-modification strategy with uncertainty from experts’ input to increase the robustness of the algorithm? Based on the methodology, an interactive web/application interface for doctors to input their knowledge and edit the learned graph is expected. If possible, you will assist in deriving the mathematical guarantee for the iterative algorithm in terms of convergence and stability properties. 4. Presentation and Documentation: You will prepare a high-quality manuscript for publication with a clear and scientific presentation of the results and methodology.

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Published since: 2025-09-29 , Earliest start: 2026-01-01 , Latest end: 2026-07-31

Organization Sensory-Motor Systems Lab

Hosts Paez Diego, Dr. , Paez Diego, Dr.

Topics Medical and Health Sciences , Mathematical Sciences , Information, Computing and Communication Sciences

Label-Efficient Representation Learning for EEG Time Series

This master`s thesis aims to explore self-supervised learning and transformer-based sequence models to develop label-efficient EEG representations.

Keywords

Deep Learning; Label-efficient Learning; Transformers; Sequence Models

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Master Thesis

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Published since: 2025-09-06

Organization Sensory-Motor Systems Lab

Hosts Dey Sharmita

Topics Information, Computing and Communication Sciences

Advancing Vibrotactile Sensory Stimulation for Brain Imaging

Design of comfortable textiles and stimulation protocol for a pneumatic, MRI-compatible vibrotactile stimulator

Keywords

touch sensation, tactile illusions, neurorehabilitation, MRI

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Semester Project , Internship , Master Thesis

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Published since: 2025-08-28 , Earliest start: 2025-09-15 , Latest end: 2026-06-26

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

Organization Sensory-Motor Systems Lab

Hosts Wolf Peter

Topics Medical and Health Sciences , Engineering and Technology

Kinematics and Dynamics of Human-Like Locomotion in LocoMuJoCo Simulations

This Master’s thesis at ETH Zürich focuses on benchmarking and improving humanoid locomotion policies in the LocoMuJoCo simulation environment. The project develops a complete pipeline for analyzing kinematic and dynamic metrics, enabling direct comparison of simulated humanoid locomotion with real-world human motion capture data.

Keywords

Humanoid Locomotion, MuJoCo, Motion Analysis, Deep Reinforcement Learning, Simulation

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Internship , Master Thesis , ETH Zurich (ETHZ)

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Published since: 2025-08-12 , Earliest start: 2025-09-01

Organization Sensory-Motor Systems Lab

Hosts Paez Diego, Dr.

Topics Medical and Health Sciences , Mathematical Sciences , Information, Computing and Communication Sciences , Engineering and Technology

3D Pose Estimation to Understand Orthosis Effects in Cerebral Palsy

This Master’s thesis at ETH Zürich develops a 3D pose estimation pipeline to evaluate the effects of orthotic devices on gait patterns in children with cerebral palsy (CP). Using a CP-specific dataset, the project benchmarks multiple 2D and 3D pose estimation models, fine-tunes the best-performing model, and applies time-series analysis to assess joint-level kinematic differences across healthy, CP without orthosis, and CP with orthosis conditions. The goal is to support clinical assessment through non-invasive motion analysis.

Keywords

3D Pose Estimation, Cerebral Palsy, Gait Analysis, Orthosis, Deep Learning

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Internship , Master Thesis , ETH Zurich (ETHZ)

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Published since: 2025-07-23 , Earliest start: 2025-07-23 , Latest end: 2025-09-30

Organization Spinal Cord Injury & Artificial Intelligence Lab

Hosts Paez Diego, Dr. , Paez Diego, Dr.

Topics Medical and Health Sciences , Information, Computing and Communication Sciences

Internship / Research assistant / Semester project in Biosensing for Robotics Care and Body Simulation (~12 months)

Join a team of scientists improving the long-term prognosis and treatment of Spinal Cord Injury (SCI) through mobile and wearable systems and personalized health monitoring. Joining the SCAI Lab part of the Sensory-Motor Systems Lab at ETH, you will have the unique opportunity of working at one of the largest and most prestigious health providers in Switzerland: Swiss Paraplegic Center (SPZ) in Nottwil (LU).

Keywords

App development, Machine Learning, Data bases, Data engineering, Systems Engineering, Data Modelling

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Internship , Lab Practice , Student Assistant / HiWi , ETH Zurich (ETHZ)

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Published since: 2025-07-23 , Earliest start: 2025-09-01 , Latest end: 2026-12-31

Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne , IBM Research Zurich Lab , Institute for Research in Biomedicine , Hochschulmedizin Zürich , Swiss Institute of Bioinformatics , University of Lucerne , University of Zurich , Zurich University of Applied Sciences , Zurich University of the Arts , Lucerne University of Applied Sciences and Arts , Berner Fachhochschule

Organization Spinal Cord Injury & Artificial Intelligence Lab

Hosts Paez Diego, Dr. , Paez Diego, Dr. , Paez Diego, Dr.

Topics Information, Computing and Communication Sciences

Your own project ideas?

It is always possible to find a project for motivated students with own ideas in the fields of assistive healthcare technologies , augmented feedback in motor learning, and sports engineering. Please send an email to the indicated contact person of our current research pillars that most closely matches your idea.