ETH Competence Center - Competence Center for Rehabilitation Engineering and Science (RESC)Acronym | RESC | Homepage | https://resc.ethz.ch/ | Country | Switzerland | ZIP, City | | Address | | Phone | | Type | Academy | Parent organization | ETH Zurich | Current organization | ETH Competence Center - Competence Center for Rehabilitation Engineering and Science (RESC) | Child organizations | | Members | |
Open OpportunitiesCausl 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
| Understanding the differences in spine kinematics between patients with lumbar spinal stenosis and those with healthy spines, along with the implications for spinal loading, could shed light on the pathophysiology of this disease and contribute to the development of more effective treatment and rehabilitation strategies. To estimate spinal loads, a novel full-body musculoskeletal model developed in AnyBody Modeling System will be used. This model will be customized to reflect subject-specific spinal alignment and will be driven by kinematic data obtained from in vivo motion-capture measurements. Inverse dynamics simulations of patient-specific spinal postures and forward flexion trials will be performed to estimate the corresponding loads. - Biomechanics
- ETH Zurich (ETHZ), Master Thesis
| The goal of this project is to apply LLMs to teach the ANYmal robot new low-level skills via Reinforcement Learning (RL) that the task planner identifies to be missing. - Intelligent Robotics
- Master Thesis
| 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
| 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. - Biomaterials, Biomechanical Engineering, Cell Development (incl. Cell Division and Apoptosis), Cellular Interactions (incl. Adhesion, Matrix, Cell Wall), Polymers
- Bachelor Thesis, ETH Zurich (ETHZ), Internship, Master Thesis, Semester Project
| This project aims to develop a clinically usable electrode for transcutaneous vagus nerve stimulation (tVNS) therapy. The objective is to create an electrode that is biocompatible, low-impedance, and easy to use, allowing patients to apply it themselves with minimal setup time. The project involves conducting a literature review, evaluating existing designs, selecting appropriate materials, developing a prototype, and assessing its efficacy and usability in a clinical setting. The outcome will be an electrode that enhances the convenience and effectiveness of tVNS therapy, contributing to improved patient treatment adherence and outcomes. - Biomedical Engineering, Materials Engineering, Mechanical and Industrial Engineering
- Internship, Master Thesis, Semester Project
| Project Objective:
This project aims to develop a new method to integrate depth perception into robots.
Specifically we are tackling the task of navigation and locomotion for our legged robot ANYmal.
Instead of training RL policies from scratch, we would like to understand the potential performance impacts by using self-supervised training, e.g. using DINO, for monocular depth images. The training may consist of leveraging existing datasets and focuses on learning a useful representation that includes not only geometric hints but potentially also semantic information from depth analog to the recent advantages achieved using RGB images.
- Computer Hardware, Computer Perception, Memory and Attention, Computer Software, Computer Vision, Electrical and Electronic Engineering
- 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
| Foundation models are a breakthrough in the field of artificial intelligence. These models are characterized by massive size, reaching billions (even trillions) of parameters, and by the ability to be adapted to a wide variety of tasks without needing to be trained from scratch. The development of these models marks a pivotal shift in AI research and application, pushing the boundaries of what machines can understand and do. However, due to the huge size of foundation model, they are very demanding in terms of computation, memory footprint and bandwidth. For this reason, foundation models face significant computational challenges. These models are typically trained on massive clusters equipped with thousands of advanced GPUs. Moreover, they require cloud services for inference as well. - Artificial Intelligence and Signal and Image Processing
- ETH Zurich (ETHZ), Master Thesis
| We are working toward robots that transform their shape adapt to new tasks and environments.
This project will entail developing control policies in simulation and deploying them on hardware, with the goal of controlling a quadruped that can change the shape of its legs to accomplish new and useful tasks (see attached image a). - Intelligent Robotics, Robotics and Mechatronics, Simulation and Modelling
- Master Thesis, Semester Project
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