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To achieve net zero emission targets replacement of fossil fuels is inevitable. In grand scheme of things Hydrogen plays a grand role in taking a major part of new fuel economy [1]. Techno economic analysis of hydrogen energy systems presents a very bright outlook [2]. The energy transport cost will also play an important role for the ultimate energy pricing. Therefore, it becomes imperative to search for the optimal installation locations for the large/small scale hydrogen storage systems considering the environmental, geological and accidental risk factors. A Multi criteria decision making analysis coupled with GIS data would provide the most economical and safe installation sites [3]. | The aim of the project is to develop a deep learning model capable of performing 3D semantic segmentation of the different features of the middle ear, from synchrotron-based X-ray microtomography 3D volumes. Challenges include developing a high-performance algorithm for handling large data sizes (20 GB per volume). The project consists in a first phase about dataset generation followed by a second phase of development/selection of the most appropriate DL model and metrics. - Computer Vision, Image Processing, Neural Networks, Genetic Alogrithms and Fuzzy Logic, Otorhinolaryngology
- Collaboration, Master Thesis, Semester Project
| In our lab we are analysing droplet-microfluidic generated multi-spectral images of single-cell secreting multiple protein simultaneously. As multifunctionalities of immune cells are understudied we are aiming at developing further data science tools for image analysis, cell detection, multi-functionality read-out and data visualization. This student project aims at building on previous data science and software development projects which have developed a comprehensive pipeline to analyse large fluorescent imaging files from single-cell secretion experiments inside droplets. The software includes droplet detection and tracking, cell identification and scoring, protein secretion read-out and classification, grouping and subpopulation data analysis together with plotting of visualisation tools. The image analysis includes Matlab, machine learning algorithms and Python Data Science tools. - Biomechanical Engineering, Diagnostic Applications, Modeling and Simulation
- Bachelor Thesis, Course Project, ETH Zurich (ETHZ), Internship, Semester Project
| Open vocabulary video semantic segmentation (OV-VSS) aims to assign a semantic label to each pixel of each frame of the video given an arbitrary set of open-vocabulary category names. There are a number of attempts on open vocabulary image semantic segmentation (OV-ISS). However, OV-VSS does not get enough attention due to the difficulty of video understanding tasks in modeling local redundancy and global correlation. In this master thesis project, we plan to fill the gap by extending existing OV-ISS methods to OV-VSS. Specifically, we aim to develop a OV-VSS method which achieves high accuracy by using temporal information and keeps high efficiency.
- Artificial Intelligence and Signal and Image Processing
- Master Thesis
| The widespread adoption of wearable technology enables continuous monitoring of physiological parameters like activity levels, heart rate, and sleep patterns. This study investigates the relationship between wearable measures and well-being, focusing on physical and mental health as well as overall quality of life. - Artificial Intelligence and Signal and Image Processing
- ETH Zurich (ETHZ), Internship, Master Thesis, Semester Project
| This study aims to detect voice pathologies distinguishing homophonic from dysphonic labels. - Information, Computing and Communication Sciences
- Internship, Master Thesis, Semester Project
| This study aims to investigate the relationship between cough and health status among heart failure patients, recognizing cough as a potential indicator of underlying health status and symptom severity. - Information, Computing and Communication Sciences
- Internship, Master Thesis, Semester Project
| Bühler, a leading industry manufacturer in Uzwil, is partnering with ETH Zürich's Feasibility Lab to offer a unique master thesis opportunity. Throughout your thesis, you'll work hand-in-hand with a team of like-minded peers, following the principles of cross-functional teamwork and agile project planning. You can explore your interests in AI/Machine Learning, Robotics, UX, Additive Manufacturing, Food Science and more and actively define your own project scope. - Digital Systems, Environmental Technologies, Industrial Biotechnology and Food Sciences, Interdisciplinary Engineering, Manufacturing Engineering, Mechanical and Industrial Engineering
- ETH Zurich (ETHZ), Master Thesis, Semester Project
| This project aims at developing a machine learning approach (for example, using convolutional neural networks) for localizing and tracking anatomical landmarks from cardiac MR images. - Biomedical Engineering, Electrical and Electronic Engineering
- Master Thesis
| Drying (e.g. Pasta drying) is the most energy intensive process step, sometimes taking up more than 50% of the total energy consumption of a plant. Superheated steam drying could present an energy efficient alternative to classical hot-air drying systems used today. This new technology could have a massive impact on the carbon-footprint and sustainability of food-drying; making it a highly future-oriented and potentially impactful innovation. - Interdisciplinary Engineering, Manufacturing Engineering, Mechanical and Industrial Engineering
- ETH Zurich (ETHZ), Master Thesis, Semester Project
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