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Beschreibung und ökonomische Interpretation der Marktordnung eines Schweizer Agrarmarktes - Agricultural, Veterinary and Environmental Sciences
- Bachelor Thesis
| 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
| Introduction and Background
Skin cells dynamically respond to mechanical and biochemical stimuli, which influence critical processes such as proliferation, differentiation, and migration. Mechanobiology, the study of these responses, requires advanced in vitro systems to emulate physiological conditions. This project utilizes a device designed for controlled manipulation of hydrostatic pressure (0.1–1.5 kPa) and substrate stiffness (0.1–100 kPa). The system facilitates isolated and scalable experiments to analyze how the interplay of these mechanical parameters affects cell behavior.
- Biology, Engineering and Technology
- Master Thesis
| Lumbar spinal stenosis (LSS) is a condition characterized by the narrowing of the lumbar spinal canal, resulting in compression of the nerve roots or cauda equina. Patients with LSS often exhibit altered spinal kinematics and compensatory movement patterns, which can increase paraspinal muscle activity and segmental loads. This study aims to estimate the spinal loads in LSS patients using an advanced full-body musculoskeletal model within the AnyBody Modeling System, incorporating patient-specific motion-capture data. Gaining a deeper understanding of the differences in spinal kinematics between LSS patients and healthy individuals, and their effects on spinal loading, could inform more effective treatment and rehabilitation strategies. - Biomechanical Engineering
- Master Thesis
| This project explores the integration of large language models (LLMs) into a reinforcement learning (RL) pipeline to automate reward function design for the robotic disassembly of end-of-life electric vehicle (EV) batteries. By iteratively refining reward functions using LLMs, the approach aims to enhance training efficiency, accelerate learning, and improve the precision and safety of complex disassembly tasks. Utilizing Nvidia Isaac Sim for simulation and transferring skills to real-world robots, the research seeks to reduce human intervention in reward engineering, providing scalable solutions for advanced robotic manipulation in battery recycling and beyond. - Intelligent Robotics
- Master Thesis
| While we have performed some basic mechanical tests to characterize Melt electrowritten tubular scaffolds, we would like to add other mechanical tests, based on ASTM standards, that would further allow us to have a better insight into mechanical properties of MEW scaffolds as well as to compare them to other vascular grafts as well as native tissues. Therefore we are searching for a motivated student who can see themself performing practical work producing tubular scaffolds as well as implementing mechanical tests. - Biomedical Engineering, Materials Engineering, Mechanical and Industrial Engineering
- ETH Zurich (ETHZ), Internship, Master Thesis, Semester Project
| This project seeks to advance the field of legged robotics by creating a versatile and accessible co-design framework that integrates mechanical design and control optimization. - CAD/CAM Systems, Computer Graphics, Dynamical Systems, Intelligent Robotics, Mechanical and Industrial Engineering, Operations Research, Optimisation, Robotics and Mechatronics
- Master Thesis, Semester Project
| Foam Additive Manufacturing (FAM) integrates 3D printing with physical blowing agents (PBAs) to produce lightweight, porous structures. The extrusion process, which involves a polymer-PBA solution, is critical for foam formation [1]. Bubble nucleation and growth occur due to rapid pressure drops and temperature changes within the extruder nozzle. - Biology, Chemistry, Engineering and Technology, Information, Computing and Communication Sciences
- Bachelor Thesis, Master Thesis, Semester Project
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