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.
Refreshing Articular Cartilage Defects by Laser Ablation - Parameter Optimization and Validation
Cartilage damage in the knee joint can be caused by aging or repetitive actions. It can be treated by surgically removing the damaged cartilage tissue and filling the generated defect with a precisely shaped, healthy cartilage graft. Removing the defected cartilage is commonly done with surgical curettes. We are investigating the use of laser ablation for a more precise defect preparation process. With two different lasers, we managed to obain promising results regarding cell viability in live samples. However, laser parameters such as pulse frequency and energy need to be optimized towards higher cutting efficiency. Your task will be to prepare a setup to test, optimize, and validate various parameter sets using different lasers for articular cartilage ablation.
Keywords
Laser ablation, laser parameter optimization, cartilage regeneration, biomedical engineering
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Master Thesis
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Published since: 2024-03-15 , Earliest start: 2023-02-01 , Latest end: 2023-12-31
Organization Bio-Inspired RObots for MEDicine-Laboratory (BIROMED-Lab)
Hosts Duverney Cédric , Rauter Georg, Prof. Dr.-Ing. , Rauter Georg, Prof. Dr.-Ing.
Topics Engineering and Technology , Physics
Master Thesis/ Internship: Causal Machine Learning with Experts in the Loop for Spinal Cord Injury (SCI) Comorbitities
Despite the growing amount of work on applying causal discovery method 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 that 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. Based on the qualifications of the candidates, we can arrange a subsidy/allowance for covering traveling or living costs.
Keywords
Causal Discovery, Expert Knowledge, Iterative Algorithm, Spinal Cord Injury
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Semester Project , Internship , Master Thesis
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Published since: 2024-03-13 , Earliest start: 2024-04-15 , Latest end: 2024-10-15
Organization Sensory-Motor Systems Lab
Hosts Paez Diego, Dr. , Paez Diego, Dr.
Topics Medical and Health Sciences , Mathematical Sciences , Information, Computing and Communication Sciences
Temporal Graphical Modeling for Understanding and Preventing Autonomic Dysreflexia
This project will be based on the preliminary results obtained from a previous master project in causal graphical modeling of autonomous dysreflexia (AD). The focus of the extension would be two-fold. One is improving the temporal graphical reconstruction for understanding the mechanism of AD. The other one is building a forecasting framework for the early detection and prevention of AD using the graph structure we constructed before. Please refer to the attached document for more details about the task description. Based on the candidate's qualifications, funding/allowance can be provided.
Keywords
Graphical Modeling; Graph Neural Networks; Multivariate Time Series; Spinal Cord Injuries; Autonomic Dysreflexia; Wearable Sensing
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Semester Project , Internship , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2024-02-28 , Earliest start: 2024-04-01 , Latest end: 2024-10-01
Organization Spinal Cord Injury & Artificial Intelligence Lab
Hosts Paez Diego, Dr. , Li Yanke , Paez Diego, Dr. , Paez Diego, Dr.
Topics Medical and Health Sciences , Information, Computing and Communication Sciences
Disease Onset Forecasting through Graphical Modeling from Biomedical Data for Spinal Cord Injury Individuals
This project focuses on developing an explainable Artificial Intelligence (xAI) framework based on graphical modeling (GM), to enhance the capacity and capability of medical AI. Collaborating with the Swiss Paraplegic Centre (SPZ) for validation, our goal is to improve the long-term prognosis of spinal cord injury (SCI) individuals. Through medical records and a multimodal sensory monitoring system, we aim to create digital twins capable of integrating diverse data sources, guiding medical treatment, and addressing common secondary health conditions in the SCI population. The envisioned GM-based digital twin (GMDT) will represent hierarchical relations across demographic features, functional abilities, daily activities, and health conditions for SCI individuals, allowing for downstream tasks such as prediction, causal inference, and counterfactual reasoning. The assimilation and evolution between the physical and digital twins will be implemented under the GM framework, promising advancements in personalized healthcare strategies and improved outcomes for SCI people. Please refer to the attached document for more details about the task description. Based on the candidate's qualifications, funding/allowance can be provided.
Keywords
Graphical Modelling, Digital Twins, Causal Inference, Data Fusion, Multimodal Learning, Physiological Modelling, Spinal Cord Injuries, Digital Healthcare
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Semester Project , Internship , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2024-02-28 , Earliest start: 2024-03-15 , Latest end: 2024-09-30
Applications limited to TU Dresden , TU Darmstadt , TU Berlin , Technische Universität München , Technische Universität Hamburg , RWTH Aachen University , Max Planck Society , Ludwig Maximilians Universiy Munich , Humboldt-Universität zu Berlin , Eberhard Karls Universität Tübingen , Universität zu Lübeck , Imperial College London , UCL - University College London , University of Oxford , University of Cambridge , Delft University of Technology , Zurich University of Applied Sciences , Wyss Translational Center Zurich , University of Zurich , Swiss Institute of Bioinformatics , IBM Research Zurich Lab , ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne , Empa , Corporates Switzerland , Zurich University of the Arts , University of St. Gallen , University of Lausanne , University of Geneva , University of Fribourg , University of Berne , University of Basel , Swiss National Science Foundation , Swiss Federal Institute for Forest, Snow and Landscape Research , Paul Scherrer Institute , CERN , Department of Quantitative Biomedicine , Eawag , University of Konstanz , University of Cologne , University of Erlangen-Nuremberg , University of Hamburg , Universtity of Bayreuth , Universität Ulm , Universität der Bundeswehr München , Social Science Research Center Berlin , National Institute for Medical Research , Royal College of Art , University of Leeds , University of Manchester , University of Nottingham , University of Aberdeen , Utrecht University , Radboud University Nijmegen , Maastricht Science Programme , Stanford University , Yale University , CNRS - Centre national de la recherche scientifique , Massachusetts Institute of Technology , Max Planck ETH Center for Learning Systems , The University of Tokyo , Tsinghua University , Peking University , Politecnico di Milano , Princeton University , Harvard , University of Toronto , University of Copenhagen , University of California, Berkeley , The University of Edinburgh , Technical University of Denmark , The University of Melbourne , The Australian National University , National University of Singapore , Nanyang Technological University
Organization Spinal Cord Injury & Artificial Intelligence Lab
Hosts Li Yanke , Paez Diego, Dr. , Paez Diego, Dr.
Topics Information, Computing and Communication Sciences , Engineering and Technology , Behavioural and Cognitive Sciences
Master Thesis / Internship: Automated Time Series Analysis in Urinary Tract Assessment in Spinal Cord Injury
The primary objective of this project is to develop an automated pipeline for the identification and recognition of patterns within urodynamic recordings, utilizing urodynamic recording data in conjunction with annotated patterns provided by experts. This endeavor seeks to reduce the susceptibility of interpreting urodynamic recordings to potential errors arising from human judgment and inaccuracies, thereby improving the management of urinary tract complications in patients with spinal cord injury. By implementing a systematic approach to pattern recognition in Bladder Valomue/Pressure Time Series Measurements of urodynamic data, the potential for error in decision-making can be significantly reduced.
Keywords
Spinal Cord Injury, Machine Learning, Deep Learning, Pattern Recognition, Feature Engineering, Time Series Analysis, Signal Processing
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Semester Project , Internship , Master Thesis
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Published since: 2024-02-22 , Earliest start: 2024-04-01 , Latest end: 2024-12-31
Applications limited to Agroscope , Berner Fachhochschule , CERN , Corporates Switzerland , CSEM - Centre Suisse d'Electronique et Microtechnique , Department of Quantitative Biomedicine , Eawag , Empa , EPFL - Ecole Polytechnique Fédérale de Lausanne , ETH Zurich , Fernfachhochschule , Forschungsinstitut für biologischen Landbau (FiBL) , Friedrich Miescher Institute , Hochschulmedizin Zürich , IBM Research Zurich Lab , Institute for Research in Biomedicine , Lucerne University of Applied Sciences and Arts , NCCR Democracy , NGOs Switzerland , Pädagogische Hochschule St.Gallen , Paul Scherrer Institute , Physikalisch-Meteorologisches Observatorium Davos , Sirm Institute for Regenerative Medicine , Swiss Federal Institute for Forest, Snow and Landscape Research , Swiss Institute of Bioinformatics , Swiss National Science Foundation , SystemsX.ch , Università della Svizzera italiana , Université de Neuchâtel , 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 , Wyss Translational Center Zurich , Zurich University of Applied Sciences , Zurich University of the Arts , University of Konstanz , Technische Universität München , TU Berlin , Eberhard Karls Universität Tübingen , European Molecular Biology Laboratory (EMBL) , FH Aachen , Humboldt-Universität zu Berlin , Justus Liebig University, Gießen , Ludwig Maximilians Universiy Munich , Martin Luther Universitat, Halle , Max Delbruck Center for Molecular Medicine (MDC) , Max Planck Society , Otto Von Guericke Universitat, Magdeburg , RWTH Aachen University , Social Science Research Center Berlin , Technische Universität Hamburg , TU Darmstadt , TU Dresden , Universität der Bundeswehr München , Universität Ulm , Universität zu Lübeck , University of Cologne , University of Erlangen-Nuremberg , University of Hamburg , Universtity of Bayreuth , Delft University of Technology , Maastricht Science Programme , Radboud University Nijmegen , Utrecht University , Max Planck ETH Center for Learning Systems , European Molecular Biology Laboratory , IEE S.A. Luxembourg , Istituto Italiano di Tecnologia , Technical University of Denmark , Technion - Israel Institute of Technology , University of Southern Denmark , Imperial College London , UCL - University College London , University of Oxford , University of Cambridge , National Institute for Medical Research
Organization Spinal Cord Injury & Artificial Intelligence Lab
Hosts Paez Diego, Dr. , Paez Diego, Dr.
Topics Medical and Health Sciences , Information, Computing and Communication Sciences , Engineering and Technology
Internship/ Master Thesis: Machine Learning for Assessment of Walking Patterns in the SCI population - Time Series Classification
Gait patterns in multiple impairments present unique and complex patterns, which hinders the proper quantitative assessment of the walking ability for chronic ambulatory conditions when translated to daily living. In this project, we will focus on finding clusters of gait patterns through unsupervised learning from a large dataset of incomplete spinal cord injury individuals. The goal is to investigate hidden patterns in relation to the type of injuries and find their application for future diagnosis and rehabilitation treatment. Your work will guide future rehabilitation methods in general clinical practice, through applied classification and dimensionality reduction in Biomechanics of walking. Goal: Develop an unsupervised clustering pipeline for a large dataset of gait patterns from spinal cord injured individuals for class similarity evaluation
Keywords
Medical and health science, computing and computational science, engineering and technology, information, machine learning, data science, data engineering
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Internship , Bachelor Thesis , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2024-02-22 , Earliest start: 2023-01-29 , Latest end: 2023-10-31
Applications limited to EPFL - Ecole Polytechnique Fédérale de Lausanne , ETH Zurich , CERN , Corporates Switzerland , IBM Research Zurich Lab , NGOs Switzerland , Zurich University of Applied Sciences , Wyss Translational Center Zurich , University of Zurich , University of St. Gallen , University of Lucerne , University of Lausanne , University of Geneva , University of Fribourg , University of Berne , University of Basel , Université de Neuchâtel , Università della Svizzera italiana , Swiss National Science Foundation , Swiss Institute of Bioinformatics , Empa , Eawag , TU Berlin , Technische Universität München , Technische Universität Hamburg , RWTH Aachen University , Max Delbruck Center for Molecular Medicine (MDC) , Delft University of Technology , UCL - University College London , University of Cambridge , University of Oxford , University of Leeds , University of Manchester , University of Nottingham , National Institute for Medical Research , Imperial College London , Radboud University Nijmegen , Maastricht Science Programme
Organization Sensory-Motor Systems Lab
Hosts Paez Diego, Dr. , Paez Diego, Dr.
Topics Medical and Health Sciences , Information, Computing and Communication Sciences , Engineering and Technology
Internships (Industrial or Research) on Body Modelling and Sensing Technology for Health Care in SCI
This hands-on work (internship or semester project) within a clinical setting will bring you close to intelligent health management while exploring multiple data systems. You will experience multimodal data of robotics rehabilitation, general clinical practice, and detailed clinical studies applied in classification and dimensionality reduction.
Keywords
Machine learning, time-series, HR, ECG, BP, wearables, nearables, Medical and health science, healthcare, Android studio, App development
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Semester Project , Internship , Lab Practice , Bachelor Thesis , Master Thesis , Other specific labels , ETH Zurich (ETHZ)
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Published since: 2024-01-27 , Earliest start: 2023-08-01 , Latest end: 2024-05-31
Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne , Empa , Eawag , Zurich University of the Arts , Zurich University of Applied Sciences , Wyss Translational Center Zurich , University of Zurich , University of St. Gallen , University of Lucerne , University of Lausanne , University of Geneva , University of Fribourg , University of Berne , University of Basel , Lucerne University of Applied Sciences and Arts , Institute for Research in Biomedicine , IBM Research Zurich Lab , Swiss Institute of Bioinformatics , CSEM - Centre Suisse d'Electronique et Microtechnique , Corporates Switzerland , CERN , Hochschulmedizin Zürich , Université de Neuchâtel , Università della Svizzera italiana , Swiss National Science Foundation , University of Konstanz , University of Hamburg , University of Erlangen-Nuremberg , University of Cologne , Universität zu Lübeck , Universität Ulm , Universität der Bundeswehr München , TU Dresden , TU Darmstadt , TU Berlin , Technische Universität Hamburg , Max Planck Society , Otto Von Guericke Universitat, Magdeburg , RWTH Aachen University , Ludwig Maximilians Universiy Munich , Humboldt-Universität zu Berlin , European Molecular Biology Laboratory (EMBL) , Eberhard Karls Universität Tübingen , Max Delbruck Center for Molecular Medicine (MDC) , Technische Universität München , Imperial College London , National Institute for Medical Research , Royal College of Art , UCL - University College London , University of Aberdeen , University of Cambridge , University of Manchester , University of Nottingham , University of Oxford , University of Leeds , Delft University of Technology , Maastricht Science Programme , Radboud University Nijmegen , Utrecht University
Organization Sensory-Motor Systems Lab
Hosts Paez Diego, Dr. , Paez Diego, Dr. , Paez Diego, Dr.
Topics Medical and Health Sciences , Information, Computing and Communication Sciences , Engineering and Technology
DEVELOPMENT OF A SENSOR UNIT FOR UNOBTRUSIVE NOCTURNAL HEALTH MONITORING
Obstructive sleep apnea (OSA) affects 1 billion individuals globally. By developing an advanced sensor unit for unobtrusive, home-based monitoring, we want to collect sleep-related data and identify unique OSA features associated with treatment outcomes. The ultimate goal is to enhance personalized care, improve diagnosis, and optimize the efficacy of obstructive sleep apnea treatments.
Keywords
Sleep Apnea Sensor Unit Nocturnal Health Monitoring Continuous Positive Airway Pressure (CPAP) Unobtrusive Monitoring Automatic Data Handling Interdisciplinary Project Data Integrity Healthcare Technology
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Published since: 2024-01-11 , Earliest start: 2024-01-14
Organization Sensory-Motor Systems Lab
Hosts Breuss Alexander
Topics Engineering and Technology
Development of a Synchronization Pipeline for Multi-Modal, Multi-Source Timeseries Data: Collaboration with Tohoku University, Japan. + Potential option for a research stay in Japan.
Modern robots collect data from various sensors. When these sensors operate independently, time-synchronization through rectification of their individual clocks and correction for temporal drift is required. In our previous work, we developed an initial version of a synchronization pipeline in Python, designed for offline data synchronization. Our current pipeline already effectively synchronizes sensors that include a common external synchronization signal. Despite already working well, our current pipeline still requires some expertise to configure the data sources. To make the pipeline widely usable, we now need to make it function seamlessly even without expert knowledge and access to external synchronization signals. This enhancement should also extend to scenarios involving continuous online data as well. Furthermore, we want to prove the correctness of the synchronization and showcase the performance based on synthetic data. In essence, your thesis will comprise the following key objectives: 1. Understand the challenges involved in data synchronization. 2. Familiarize yourself with the existing synchronization pipeline. 3. Innovate strategies for achieving data synchronization without relying on external synchronization signals. 4. Enhance the user interface by creating an intuitive guide for using the pipeline effectively. 5. Extend the functionality to accommodate online data streams. 6. Assess the pipeline's correctness and performance using synthetic biosignals, as well as pre-recorded biosignals from the SMS-Lab and Tohoku University. Throughout this project, you will receive guidance from me, a 4th year PhD candidate at the Sensory-Motor Systems Lab at ETH Zurich, and researchers at Tohoku University in Sendai, Japan. As I will be in Japan from October, we will conduct the weekly meetings over Zoom. Furthermore, in case of interest, you have the exciting opportunity to visit us in Japan. This opportunity can be pursued either through personal funding or by applying for respective scholarships, such as the Heyning-Roelli Foundation, SEMP, Spickenreuther Foundation, and others. I have received scholarships in the past and I am happy to provide guidance and support throughout the application process.
Keywords
Signal processing, data synchronization, computer science, data validation, benchmarking, software engineering, master thesis, research stay, international collaboration
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Semester Project , Internship , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2024-01-11 , Earliest start: 2023-10-08
Organization Sensory-Motor Systems Lab
Hosts Breuss Alexander
Topics Information, Computing and Communication Sciences , Engineering and Technology
Development of a miniature milling feed mechanism for a miniature intraoral robot
The goal of this project is to develop a miniature milling feed mechanism that allows the milling instrument to move in the vertical direction for a miniature intraoral robot.
Keywords
Surgical robotics, Minimally invasive, Intraoral robot, Prototype development
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Published since: 2023-11-17 , Earliest start: 2023-06-25
Organization Bio-Inspired RObots for MEDicine-Laboratory (BIROMED-Lab)
Hosts Rauter Georg, Prof. Dr.-Ing. , Rauter Georg, Prof. Dr.-Ing. , Tomooka Yukiko
Topics Engineering and Technology
Object Slippage Detection for a Miniature Force-Sensitive Gripper
We are developing a teleoperated micro-assembly system. A core component is a force-sensitive micro-gripper. A first gripper prototype has been realized and evaluated. Your task will be to review and improve the current design and to implement automated object slippage detection.
Keywords
Micro-manipulation, robotic gripper, force sensing, slippage detection, teleoperation
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Master Thesis
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Published since: 2023-11-14 , Earliest start: 2023-02-01 , Latest end: 2023-12-31
Organization Bio-Inspired RObots for MEDicine-Laboratory (BIROMED-Lab)
Hosts Duverney Cédric , Rauter Georg, Prof. Dr.-Ing. , Rauter Georg, Prof. Dr.-Ing.
Topics Engineering and Technology
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.