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The project focuses on the development of novel drug delivery systems with a focus on creating technologies that will help people with difficult diseases or improve quality of life. The project involves the development of novel mechanical delivery mechanisms for different drug compounds. Innovations in this space can improve adherence to drug regimens and even reduce side-effects by creating well controlled drug release dosages over time. The ideal candidate will be eager to learn and work in a multidisciplinary project that involve aspects of mechanical engineering, material science, chemical engineering, bio-analytics, and bioengineering. This merger of engineering disciplines makes for an interesting learning experience that goes beyond the typical classroom and more into the world of translational and applied engineering and science. - Biomedical Engineering, Chemical Engineering, Materials Engineering, Mechanical and Industrial Engineering
- Bachelor Thesis, Internship, Master Thesis
| This project investigates the integration of real-time computer vision capabilities into modern video see-through head-mounted displays (VST-HMDs). Using the Android Debugging Bridge (ADB), the study aims to develop a prototype for achieving object tracking, image registration, and other vision-based applications on Quest 3. Leveraging the computational power of a connected PC, the project explores advanced uses of VST technology, contributing to augmented reality (AR) innovations with applications such as diminishing reality or visual search. - Virtual Reality and Related Simulation
- Bachelor Thesis, ETH Zurich (ETHZ), Semester Project
| Robots have become increasingly advanced recently, capable of performing challenging tasks such as taking elevators and cooking shrimp. Moreover, their ability to accomplish long-horizon tasks given simple natural language instructions is also made possible by large language models. However, with this increased functionality comes the risk that intelligent robots might unintentionally or intentionally harm people based on instructions from an operator. On the other hand, significant efforts have been made to restrain large language models from generating harmful content. Can these efforts be applied to robotics to ensure safe interactions between robots and humans, even as robots become more capable? This project aims to answer this question.
- Engineering and Technology, Information, Computing and Communication Sciences
- Master Thesis, Semester Project
| This study investigates depth perception with virtual and real objects using video see-through (VST) and optical see-through (OST) head-mounted displays (HMDs). By comparing devices like Meta Quest 3, Pico 4, and HoloLens 2, the research explores how humans perceive spatial depth in mixed reality (MR) scenarios. Through Unity-based application development and user studies, the work evaluates depth perception differences and provides insights for advancing MR technology. - Virtual Reality and Related Simulation
- Bachelor Thesis, ETH Zurich (ETHZ), Semester Project
| Our team develops novel Aerial Robots that are able to autonomously manipulate and perform work in flight. In this thesis, we would like to explore the learning of task-specific policies for manipulation in flight.
- Intelligent Robotics
- Master Thesis
| Recent research has expanded the investigation of spontaneous fluctuations in the BOLD fMRI signal from the brain to the spinal cord. The vast majority of the studies have focused on the cervical cord, neglecting the lumbar cord which is involved in lower limb control as well as bladder, bowel, and sexual function. In a previous project, we demonstrated the presence of resting-state networks in the lumbar cord. Now, we aim to investigate the reliability of these resting-state networks within and across scans. Another goal is to improve the processing of the BOLD fMRI data, which requires an in-depth comparison of different denoising strategies and exploring their impact on reliability. To achieve these goals, the Master's student will have access to existing resting-state BOLD fMRI data and will also have the opportunity to expand the dataset by acquiring additional data. - Medical Biotechnology, Neurology and Neuromuscular Diseases, Neurosciences, Radiology and Organ Imaging, Rehabilitation Engineering
- Master Thesis
| Cardiac diffusion tensor imaging (cDTI) provides information about the cardiac microstructure by measuring the diffusion of water molecules within the heart wall. Current imaging standards measure three slices distributed across the left ventricle. However, if not corrected, respiratory motion causes slice misalignments that obstruct microstructure inference. Yet, this motion might also allow us to estimate sample points between slices, thus adjusting for motion and increasing spatial coverage. By using the respiratory navigator data, you will map in-vivo cDTI data to a 3D digital twin mesh and implement a tensor estimation to estimate sample points between slices based on spatial smoothness regularization. You then perform an accuracy evaluation on simulated data. - Biomedical Engineering, Human Biophysics, Medical Physics
- Bachelor Thesis, Master Thesis, Semester Project
| This thesis explores the integration of multi-view data and citizen science images to develop a scalable, high-accuracy tree species identification framework. - Computer Vision, Forestry Sciences, Photogrammetry and Remote Sensing
- Master Thesis
| The goal of the project consists in deriving error bounds for the approximate Gaussian process regression method given by the FITC method. - Engineering and Technology
- Master Thesis, Semester Project
| Multi-task learning is the problem of jointly learning multiple functions
that are “related” to each other. By leveraging this similarity, estimation performance can be
improved on each (possibly unseen) task, and one can make an efficient use of the available
data. The project aims at deriving
uncertainty bounds around the multi-task-system estimates. Specifically, the candidate will
work with the regularized trigonometric regression inspired by the so-called sparse-spectrum
Gaussian process regression, investigate the issue of bias learning (i.e., finding the features
that encode similarity among tasks) and derive error bounds for it, possibly setting the analysis
in the statistical learning framework. - Engineering and Technology
- Master Thesis, Semester Project
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