ETH Zurich

Open Opportunities

Exploring Computer Vision on Video See-Through Head-Mounted Displays ETH Zurich Innovation Center Virtual Reality 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

Securing the Future: Enhance Human Safety in Robot Decision Making ETH Zurich Robotic Systems Lab 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

Depth Perception of Virtual and Physical Objects for VST- and OST-HMD ETH Zurich Innovation Center Virtual Reality 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

Vison- and Depth-based Reinforcement Learning for dexterous and accurate manipulation with Aerial Robots [multiple openings] ETH Zurich Autonomous Systems Lab 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

Cardiac Diffusion Tensor Imaging (cDTI) Inference on Digital Twins ETH Zurich Cardiovascular Magnetic Resonance 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

Master thesis: Enhancing tree species identification using multi-view Convolutional Neural Networks ETH Zurich Forest Resources Management 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

Error bounds for scalable Gaussian process regression ETH Zurich Research Zeilinger 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

Error bounds for Regularized Trigonometric Regression in the Multi-task setting ETH Zurich Research Zeilinger 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

Learning-based stochastic Model Predictive Control with scalable Gaussian process regression ETH Zurich Research Zeilinger One of the key ingredients in Model Predictive Control (MPC) schemes is an effective model of the dynamical system’s response to external inputs. However, first- principles models are often not accurate enough, as there might be unknown external disturbances and model mismatches. To address this, learning-based control aims at complementing nominal models with data-based ones, which can be refined online as new system observations are gathered. Thus, such a model should be both expressive and fast to update. This project focuses on a learning-based stochastic MPC scheme, where uncertainty in the model is learned with an approximate Gaussian process, namely the regularized trigonometric regression stemming from the so- called sparse-spectrum Gaussian processes. To this aim, the candidate will review the available uncertainty bounds around these approximate Gaussian-process-based estimates and incorporate them in the MPC formulation. The chance-constraints thereby obtained are then to be analyzed to rigorously prove recursive feasibility and stability of the closed-loop system. Engineering and Technology Master Thesis, Semester Project

Organ-on-Chip Automation Hardware Design: Development and Integration of a Miniature Robotic XY Stage for a Miniature Microscope ETH Zurich Sensory-Motor Systems Lab Organs-on-Chip (OoC) replicate human organs in vitro but often lack physiological perfusion profiles. This project aims to develop a compact robotic XY stage for integration with OoC perfusion systems, enhancing automation and precision. By improving compatibility and mimicking dynamic blood flow, this innovation advances research and pharmaceutical applications. Biomedical Engineering Master Thesis