 Sensory-Motor Systems LabOpen OpportunitiesThe project shall enable all people – regardless of their physical or cognitive abilities – to work in the kitchen - Engineering and Technology, Medical and Health Sciences
- Master Thesis, Semester Project
| The project aims to describe vibration of internal organs during jogging and what benefits these vibrations may have for human health. - Biomedical Engineering
- Master Thesis
| 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. - Expert Systems, Health Information Systems (incl. Surveillance), Statistics
- Internship, Master Thesis, Semester Project
| 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. - Biomedical Engineering, Digital Systems, Knowledge Representation and Machine Learning, Pattern Recognition, Simulation and Modelling
- ETH Zurich (ETHZ), Internship, Master Thesis, Semester Project
| 12-lead electrocardiograms (ECGs) are still solely documented on paper in many hospitals, especially in the Global South. These physical paper records provide a multitude of conditions recorded in many different countries. Our lab has access to a dataset with more than 8000 patient’s ECG photos / scans of 12-lead signals printed onto physical paper sheets. The dataset comprises 12-lead ECG image records from more than 35 hospital sites across Europe. The primary objective of this project is to develop an automated digitization pipeline from raw image scan in .png format towards 12 vectorized ECG time series in WFDB format. - Computer Vision, Engineering and Technology, Medical and Health Sciences
- Bachelor Thesis, Internship, Master Thesis, Semester Project
| 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. - Artificial Intelligence and Signal and Image Processing, Autonomic Nervous System
- ETH Zurich (ETHZ), Internship, Master Thesis, Semester Project
| This study examines transfer learning to enhance activity recognition in SCI patients using wearable sensors and existing datasets. - Artificial Intelligence and Signal and Image Processing, Biomechanical Engineering, Electrical Engineering, Human Movement and Sports Science, Mechanical Engineering, Rehabilitation Engineering
- Internship, Master Thesis
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