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Accurate modelling of glacier mass balance requires the representation of wind-driven snow transport. This thesis will investigate the performance of various parameterizations. | Implement and benchmark a reduced-complexity ice flow model to run in parallel on graphical processing units (GPUs) using the the Julia language and the finite-difference method. | Causl Discovery aims to find causal relations from data, being increasingly important in various fields such as health science. Despite the growing amount of work on applying causal discovery methods 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 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.
Besides, fusing datasets from different sources is essential for holistic discovery and reasoning. This project will also focus on developing methods of machine learning and data fusion over distinct contexts under the scope of SCI.
Based on the qualifications of the candidates, we can arrange a subsidy/allowance to cover traveling or living costs. - Expert Systems, Health Information Systems (incl. Surveillance), Statistics
- Internship, Master Thesis, Semester Project
| Snow accumulation is a critical parameter for glacier mass balance investigations. Conventional measurements include snow depth probings while ground penetrating radar (GPR) has been successfully applied for continuous measurements of the snow cover thickness. - Geophysics, Glaciology
- Bachelor Thesis, Semester Project
| Understanding the differences in spine kinematics between patients with lumbar spinal stenosis and those with healthy spines, along with the implications for spinal loading, could shed light on the pathophysiology of this disease and contribute to the development of more effective treatment and rehabilitation strategies. To estimate spinal loads, a novel full-body musculoskeletal model developed in AnyBody Modeling System will be used. This model will be customized to reflect subject-specific spinal alignment and will be driven by kinematic data obtained from in vivo motion-capture measurements. Inverse dynamics simulations of patient-specific spinal postures and forward flexion trials will be performed to estimate the corresponding loads. - Biomechanics
- ETH Zurich (ETHZ), Master Thesis
| This project aims to transform the way users engage with data in spreadsheets by creating an interactive, collaborative platform for data storytelling. The motivation is to make data analysis more engaging, accessible, and narrative-driven, allowing users to seamlessly weave stories around their data while leveraging the power of large language models (LLMs). The platform will feature a community-based web forum where users can access a variety of public datasets or upload their own. Each dataset can be imported into a storytelling system integrated within the spreadsheet environment. This system will allow users to perform common data operations, such as calculating averages, standard deviations, and generating visualizations (e.g., charts), while the LLM will offer suggestions for a cohesive narrative. For instance, the system can auto-generate explanatory text based on the user’s data manipulations, creating a comprehensive story of the dataset. The goal is to help users articulate insights, uncover trends, and communicate findings in a more structured and compelling way. (Target Venue: ACM UIST 2025)
- Interdisciplinary Engineering
- ETH Zurich (ETHZ), Master Thesis
| The TanDEM-X mission has been launched for more than a decade. The mission collected rich valuable data that allows for generating high resolution digital elevation models (DEM) and analysing the change of earth surface topography. Therefore, this project aims to leverage the TanDEM-X data collected over the Great Aletsch Glacier to evaluate the height changes and velocity dynamics of the glacier. - Glaciology, Photogrammetry and Remote Sensing
- Master Thesis
| Glaciers are an essential freshwater resource, especially during warm and dry periods. Assessing the contributions of glacier melt to downstream discharge at daily to weekly scales is important to understand the susceptibility of mountain water supply to the ongoing and future projected glacier retreat. However, these contributions are difficult to obtain as glacier mass balance observations are typically multi-year, annual or seasonal measurements. Therefore, daily streamflow dynamics will be analysed. - Glaciology
- Bachelor Thesis, Master Thesis
| This project seeks to raise awareness among young people about the presence and influence of algorithmic bias in social media apps. Youth often interact with these platforms without fully understanding how their interactions contribute to biased content curation. To address this, we propose the development of a web-based system that simulates a social media app, providing real-time feedback on how user-generated content can influence algorithmic bias. The web system will function as a mock social media platform where users, particularly young people, can post content as they would on real social media apps. When users submit posts, the system will generate a preview that shows how the content spreads through the platform. This preview will visually represent how the content engages with algorithms, potentially amplifying biases based on factors like language, content type, or context. By seeing this in action, users will learn how their posts can unintentionally reinforce algorithmic bias, encouraging them to be more mindful of their contributions. (Target Venue: ACM UIST 2025)
- Interdisciplinary Engineering
- Bachelor Thesis, ETH Zurich (ETHZ), Master Thesis
| The goal of this project is to apply LLMs to teach the ANYmal robot new low-level skills via Reinforcement Learning (RL) that the task planner identifies to be missing. - Intelligent Robotics
- Master Thesis
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