Max Planck ETH Center for Learning Systems| Acronym | 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 OpportunitiesHydrocephalus is a medical condition characterized by the disturbed dynamics of cerebrospinal fluid (CSF) and its excessive accumulation in the brain ventricles. In contemporary therapy, a shunt system is implanted that drains CSF from the ventricles into the peritoneal space. While various types of shunt systems exist, they are essentially all based on passive mechanical pressure valves that are driven by the external pressure gradient. This limits the efficacy of these shunts and complications such as over- and underdrainage may occur. To improve the therapy of hydrocephalus, we are working towards intelligent mechatronic shunt systems that are capable of monitoring vital signs and adapting CSF drainage according to the patient’s actual needs. In this project, you will support the technical upgrade of an existing hardware-in-the-loop test bench that is used for the evaluation of existing shunt systems and the development of smart shunt system. - Biomedical Engineering, Dynamical Systems, Electrical Engineering, Mechanical Engineering, Systems Theory and Control
- Student Assistant / HiWi
| This project will be carried out in collaboration with the FHNW Institute for Sensors and Electronics
Monitoring plant health is crucial for early detection of pests, identifying anomalies, and ensuring timely interventions. While numerous sensors are available for this purpose, selecting the most effective ones and eliminating redundancy remains a challenge. Additionally, transmitting large volumes of data to the cloud is power-intensive, especially in resource-constrained environments. To address these challenges, local preprocessing is essential to reduce data load and enhance efficiency. Leveraging neuromorphic hardware provides a promising approach to achieve low-power, real-time processing for plant status monitoring. - Engineering and Technology
- Bachelor Thesis, Master Thesis, Semester Project
| Neuromorphic computing represents a cutting-edge approach to designing computational systems by mimicking the architecture and functionality of biological neurons. One of the persistent challenges in fabricating neuromorphic devices is the cross-device response variability, which is often seen as a limitation. However, biological neurons and synapses are intrinsically heterogeneous, exhibiting a wide spectrum of responses that enhance robustness and adaptability. Inspired by this, recent computational study[1] demonstrated that neural networks composed of heterogeneous neurons—without the need for plasticity—significantly outperform their homogeneous counterparts, particularly in their reliability across a range of temporal tasks.
[1] Golmohammadi et al 2024, https://arxiv.org/abs/2412.05126
[2] Zendrikov et al, 2023 10.1088/2634-4386/ace64c
- Engineering and Technology
- Master Thesis, Semester Project
| Water management is critical to agriculture, especially with increasing climate change and water scarcity concerns. Traditional water supply systems often rely on fixed schedules or basic sensor feedback to control irrigation or water distribution, which can result in inefficiencies and wastage. With the rapid advancements in machine learning and neuromorphic computing, there is an opportunity to develop smarter, more adaptive water supply systems. Spiking Neural Networks (SNNs) on a hardware substrate[1], inspired by the brain's efficient way of processing information, offer a promising solution due to their event-based processing and low energy consumption.
This project proposes the development of a neuromorphic implementation of a Spiking Neural Network on DYNP-SE1[2] to optimize water supply using real-time moisture datasets [3]. The SNN will learn and adapt to environmental changes, ensuring that water is supplied only when necessary, reducing waste, and optimizing water usage. - Agricultural Engineering
- Bachelor Thesis, Master Thesis, Semester Project
| 30 June - 29 August 2025. The ETH Robotics Student Fellowship (ETH RSF) program offers graduate students the opportunity to research alongside experts on the specific topic of robotics of their choice. This fellowship takes place during summer and is open to all students worldwide. - Engineering and Technology, Information, Computing and Communication Sciences
- Internship, Lab Practice, Summer School
| This project aims to fabricate and characterize thin films of ceramic oxides (with thickness in the nanometer range) which have potential applications in biomedical devices, such as implanted flexible electronics (Fig. a) and functional microrobots (Fig. b). - Condensed Matter Physics-Electronic and Magnetic Properties; Superconductivity, Materials Engineering, Mechanical and Industrial Engineering
- Bachelor Thesis, Master Thesis, Semester Project
| This project reconstructs liquids from multi-view imagery, segmenting fluid regions using methods like Mask2Former and reconstructing static scenes with 3D Gaussian Splatting or Mast3r. The identified fluid clusters initialize a particle-based simulation, refined for temporal consistency and enhanced by optional thermal data and visual language models for fluid properties. - Computer Vision
- Master Thesis, Semester Project
| This project addresses the computational bottlenecks in model-free reinforcement learning (RL) with high-dimensional image inputs by optimizing Gaussian Splatting—a GPU-accelerated technique for photorealistic image generation from point clouds—for RL applications. By integrating pre-sorting methods, the project aims to enhance rendering speeds, enabling broader RL applications beyond geometric constraints or abstraction layers. Building on previous work involving risk annotations in Gaussian splats, the project seeks to develop generalizable RL policies that leverage real-world knowledge. - Intelligent Robotics
- Master Thesis, Semester Project
| This project aims to develop an online learning framework for achieving precise position control of a soft robotic arm while adapting to time-varying system dynamics. - Engineering and Technology
- Master Thesis
| This project aims to develop a neuromorphic system for object classification using tactile data, inspired by the human sense of touch. By integrating biomimetic sensors and a neuromorphic chip, the system processes spatiotemporal tactile information with high efficiency and low power consumption. The approach leverages spiking neural networks (SNNs) to encode and shapes in real time. The project focuses on designing algorithms optimized for the unique properties of neuromorphic hardware and evaluating performance in dynamic, real-world scenarios. This work has potential applications in robotics, prosthetics, and intelligent sensing systems, offering an energy-efficient solution for tactile perception tasks.
- Engineering and Technology, Medical and Health Sciences
- Bachelor Thesis, Master Thesis, Semester Project
|
|
