Max Planck ETH Center for Learning SystemsAcronym | MPG ETH CLS | Homepage | http://learning-systems.org/ | Country | [nothing] | ZIP, City | | Address | | Phone | | Type | Alliance | Current organization | Max Planck ETH Center for Learning Systems | Members | |
Open OpportunitiesLucid dreaming allows individuals to be aware of and control their dreams, offering benefits for therapy, creativity, and skill enhancement. Techniques such as pre-sleep meditation improve lucid dream frequency and clarity. Sensory stimuli, synchronized with sleep stages, can further induce lucidity. This project aims to develop a closed-loop system to enhance lucid dreaming training and experience. The system will use recorded EEG data to detect sleep stages and combine pre-sleep meditation with sensory stimuli applied during the respective sleep stages. The lab's existing rocking bed “Somnomat” will be integrated to apply vestibular stimuli and additional sensory stimuli based on auditory or visual cues, triggered by physiological changes, or depending on a specific sleep stage. - Engineering and Technology
- ETH Zurich (ETHZ), Master Thesis
| Latent diffusion models (LDMs) [1] have recently emerged as a powerful tool for high-quality image generation, offering superior scalability and training efficiency compared to pixel-space diffusion models. While the network architectures of LDMs have received significant attention, other design aspects of these models (for example the forward noise schedule and the autoencoder) remain underexplored. This project aims to enhance the characteristics of LDMs, e.g., quality and efficiency, by investigating various design elements of latent diffusion models.
- Information, Computing and Communication Sciences
- ETH Zurich (ETHZ), 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
| Model Predictive Control (MPC) is extensively utilized in industry and academia. However, designing an optimal cost function and constraints for achieving the best closed-loop performance remains an open challenge. This project seeks to bridge this gap by framing the problem as a policy optimization problem and solving it through the application of gradient-based optimization schemes. - Electrical Engineering
- Master Thesis, Semester Project
| The project focuses on identifying distinct object instances (such as tables and chairs) within 3D reconstructions of indoor environments. - Computer Vision
- Master Thesis, Semester Project
| Digital humans are a very popular and fast-growing area with manifold applications in AR/VR. However, the dynamic of the existing head avatars is mostly limited to the facial region. In this project, we will focus on realistically rendered avatars that would include realistic modeling of both faces and hair with physically accurate dynamics and interactions. - Information, Computing and Communication Sciences
- ETH Zurich (ETHZ), Master Thesis, Semester Project
| 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. - 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
| The process of evaluating sleep examinations and diagnosing sleep disorders through polysomnographies (PSGs) is labor-intensive as it requires manual analysis from sleep technicians and doctors. In collaboration with Clinic Barmelweid, a leading sleep and rehabilitation clinic in northwestern Switzerland, we plan to automate this process using machine learning models. Clinic Barmelweid conducts approximately 400-450 PSGs annually and has access to a dataset of more than 5,000 recordings. - Artificial Intelligence and Signal and Image Processing, Biomedical Engineering, Medical and Health Sciences
- Collaboration, ETH Zurich (ETHZ), Internship, Master Thesis, Semester Project
| The Multi-Scale Robotics Lab develops novel actuation methods for endoscopic devices utilizing magnetic navigation systems (MNS). In MNS, an external magnetic field applies forces and torques on magnets attached to the endoscopes. To control these endoscopes, precise shape estimation techniques are required. Current methods try to estimate the endoscope’s shape by measuring the external field along the endoscope using hall-sensors. This method requires precise knowledge about the applied external field and often lacks in localization precision in certain directions.
- Electrical Engineering, Mechanical Engineering
- Master Thesis
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