University of Zurich

Open Opportunities

Fluid-structure interaction of the aortic valve. ETH Zurich Cardiovascular Magnetic Resonance The aim of the project is to investigate the possible implementation of a numerical model of the aortic valve in a fluid-structure interaction (FSI) framework and to quantify its impact on the aortic flow. Biomechanical Engineering, Mechanical Engineering Master Thesis

Asynchronous Processing for Event-based Deep Learning University of Zurich Robotics and Perception The goal of this project is explore ways to adapt existing deep learning algorithms to handle sparse asynchronous data from events. Engineering and Technology Master Thesis, Semester Project

Learning-based Active Noise Cancellation for MRI ETH Zurich Cardiovascular Magnetic Resonance The aim of this project is to investigate the feasibility of machine learning based active noise cancellation to improve patient comfort during MRI scans. Eventually, we will build a MRI compatible prototype of noise cancelling headphones. Engineering and Technology, Medical and Health Sciences Bachelor Thesis, Master Thesis, Semester Project

Learning to Deblur Images with Events University of Zurich Robotics and Perception Explore machine learning based approaches for deblurring of images with events Computer Vision, Image Processing, Pattern Recognition Master Thesis, Semester Project

Event-based Vision for Autonomous Driving University of Zurich Robotics and Perception Collaborate with Volkswagen's research division to create a high-quality driving dataset including event cameras Information, Computing and Communication Sciences Master Thesis

Computational Photography and Videography University of Zurich Robotics and Perception Combine the complementary information from standard and event cameras to enhance images and video. Image Processing Master Thesis

Super-resolution optoacoustic tomography ETH Zurich Functional and Molecular Imaging Optoacoustic imaging systems have reached spatio-temporal resolutions beyond what is possible with other imaging modalities, while additionally providing rich optical contrast offering unique capabilities in a myriad of biological studies. However, the resolution of state-of-the art optoacoustic tomography is still limited by acoustic diffraction, which prevents the visualization e.g. of small microvascular structures in vivo. The aim of this project is the development of a method based on analyzing the fluctuations in a sequence of optoacoustic images with the aim to break through the acoustic diffraction barrier. Biomedical Engineering, Mathematical Sciences, Medical and Health Sciences, Optical Physics Master Thesis

Resource-efficient 3D convolutional neural networks for high-resolution segmentation ETH Zurich Cardiovascular Magnetic Resonance The aim of this project is to develop a resource-efficient convolutional neural network for anatomical segmentation of three-dimensional magnetic resonance scans. Engineering and Technology Bachelor Thesis, Master Thesis, Semester Project

Segmentation and in-vitro Evaluation of 5D Flow MRI ETH Zurich Cardiovascular Magnetic Resonance 5D Flow MRI offers novel ways of assessment of cardiovascular diseases through insights in blood flow hemodynamics in vessels such as the aorta. Emerging techniques include non-invasive estimation of turbulence and motion states[1] and improved estimation of pressure drop[2] in stenotic flows. Engineering and Technology, Medical and Health Sciences Bachelor Thesis, Master Thesis, Semester Project

Many-Organ Segmentation ETH Zurich Cardiovascular Magnetic Resonance There is considerable existing work on using machine learning to automatically segment the heart in magnetic resonance (MR) images. However, there is little work on simultaneously segmenting the surrounding anatomy. This project is about exploring approaches to overcome this problem. Computer Vision, Image Processing, Neural Networks, Genetic Alogrithms and Fuzzy Logic, Pattern Recognition, Simulation and Modelling Bachelor Thesis, Master Thesis