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 OpportunitiesJoin our research project focused on analysing complex neurophysiological data collected during non-invasive brain stimulation experiments. This project aims to optimise brain stimulation protocols for future stroke rehabilitation by investigating neural responses to various stimulation parameters. The data includes electrocardiogram (ECG), electroencephalogram (EEG), photoplethysmography (PPG), inertial measurement unit (IMU) readings, pupilometry, and galvanic skin response (GSR). We aim to model brain states based on these measurements to define brain circuitry outcomes from stimulation and movement interactions, using advanced techniques like connectivity-based biomarkers. This modeling will help generalise findings to broader brain states, such as valence, attention, and stress. - Applied Statistics, Biological Mathematics, Neurosciences, Simulation and Modelling
- Master Thesis
| Most control methods operate under the assumption of a known model. However, in practice, knowing the exact dynamics model a priori is unrealistic. A common approach is to model the unknown dynamics using Gaussian Processes (GPs) which can characterize uncertainty and formulate a Model Predictive Control (MPC) type problem. However, it is difficult to exactly utilize this uncertainty characterization in predictive control.
In a recent approach [1], we proposed a sampling-based robust GP-MPC formulation for accurate uncertainty propagation by sampling continuous functions. In contrast, in the proposed project, you will implement an approximation method for sampling continuous functions using a finite number of basis functions [2] and solve the MPC problem jointly with the sampled dynamics. You will analyze the trade-offs between performance, approximation accuracy, and computational cost for this method. - Engineering and Technology
- Master Thesis, Semester Project
| Our research group aims to enhance the understanding of human language acquisition and development using songbird as model.
We are particularly interested in the evolutionary aspects of language, where two developmental tendencies are observed: convergent and divergent evolution. Convergent evolution refers to the simplification of language complexity, similar to how infants gradually acquire human language. Conversely, divergent evolution involves an increase in complexity, akin to teenagers creating and using novel words to establish unique identities. We propose to investigate whether similar effects are observable in animal vocalization learning, specifically in song learning of zebra finches and to explore the effect of social interaction.
To facilitate this investigation, our team has developed a "birdpark," a multimodal recording system that provides a naturalistic social environment for observing and recording multiple zebra finches within a dynamic group context.
- Learning, Memory, Cognition and Language, Linguistic Processes (incl. Speech Production and Comprehension), Sensory Systems, Signal Processing, Zoology
- ETH Zurich (ETHZ), Master Thesis, Semester Project
| Modern power systems exhibit significant complexity, making their analysis and control particularly challenging, especially when precise system models are unavailable. Traditional model-based control strategies often fail to scale with increasing system complexity, while recent advances in nonlinear, learning based control offer promising alternatives. However, many of these methods lack formal stability guarantees, which are crucial for safety-critical applications such as power system frequency control. This project aims to bridge this gap by developing a deep learning framework for analyzing the dissipativity properties of power systems and designing stabilizing controllers with formal guarantees. - Engineering and Technology
- Master Thesis, Semester Project
| Model predictive control (MPC) is a widely used control technique that optimizes control inputs while fulfilling process constraints. Although automated tuning methods have been developed for task-specific MPC, they struggle when tasks change over time, requiring costly re-tuning. This project aims to reduce the computational burden of re-tuning by leveraging meta-learning, enabling efficient adaptation of controllers to different environments with minimal data. - Electrical Engineering
- Semester Project
| This project focuses on the generation of detailed 3D models from a user-specified set of 3D cuboids. - Computer Vision
- ETH Zurich (ETHZ), Master Thesis, Semester Project
| We are working on a novel product which tracks breathing with standard earphones (like Apple AirPods) only. To do this we capture the sound of breathing with the microphone which is in every earphone. We are working on an algorithm with which we can detect the ventilatory thresholds (VT1/VT2) with the breathing rate captured via the earphones. BreezeLabs is an ETH spin-off. - Biomedical Engineering, Sport and Exercise Psychology, Sports Medicine
- Internship, Master Thesis
| Develop a method for collision aware reaching tasks using reinforcement learning and shape encodings of the environment - Intelligent Robotics
- ETH Zurich (ETHZ), Master Thesis
| Unlock the potential of differentiable simulation on ALMA, a quadrupedal robot equipped with a robotic arm. Differentiable simulation enables precise gradient-based optimization, promising greater tracking accuracy and efficiency compared to standard reinforcement learning approaches. This project dives into advanced simulation and control techniques, paving the way for improvements in robotic trajectory tracking. - Intelligent Robotics
- Bachelor Thesis, Master Thesis, Semester Project
| 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). - Artificial Intelligence and Signal and Image Processing, Computer Software, Data Format, Information Systems
- ETH Zurich (ETHZ), Internship, Lab Practice, Student Assistant / HiWi
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