University of Zurich

Open Opportunities

InSight – an automated 3D cancer detection platform University of Zurich Institute of Neuropathology Histopathological approaches have remained the same for the past 200 years despite the limitations resulting in detrimental implications: (i) The specimen preparation (cutting to prepare thin tissue slices) is extremely laborious. (ii) Due to the laborious work, only 2% of the tissue sample is prepared and analyzed in 2D. (iii) The low sample amount and the generated 2D images limit the extractable information. These limitations culminate in a false negative cancer detection rate of up to 28%. Failure to detect up to 28% of cancer cells is unacceptable. By combining technology from biomedicine, mechanical engineering, and food science, we have developed a game changing interdisciplinary solution that mitigates the bottlenecks associated with 3D tissue sample preparation, enabling the generation of spatial images within 2 days. Biology, Chemistry, Engineering and Technology, Medical and Health Sciences Bachelor Thesis, Course Project, Internship, Master Thesis, Semester Project

Master’s Thesis: A Spine Segmentation Pipeline for MR Images using Unsupervised Domain Adaptation University of Zurich Bjoern Menze There exists an accurate and robust spine segmentation pipeline for CT images (anduin.bonescreen.de). This is a series of three networks as shown in the figure (left), that are trained in a supervised manner using thousands of annotated CT scans. Can we now transfer these networks to segment MR images without any ground truth annotations? Computer Vision Master Thesis

Cell Imaging-Based Diagnostic Platform for Patients with Rheumatic Diseases University of Zurich Bjoern Menze Background Precision medicine based on cell-based assays has gradually gained popularity and is now essential for the treatment of patients suffering from diseases with complex treatment regimens, such as rheumatoid arthritis. Classifying patients according to their synovial fibroblast (SF) functional signature could lead to targeted therapies with a much higher success rate than currently available disease-modifying drugs. To achieve this goal, we have successfully developed a series of assays that enable functional screening of synovial fibroblasts and form the basis of a drug discovery approach for more effective personalized treatment. Aim The aim of this work is to develop a model for automatically predicting the cellular stage from single-cell microscopy images, as such a model would facilitate the personalization of treatments for patients suffering from rheumatic diseases. Therefore, the functional stage of synovial fibroblasts (SF) - the cells of interest - should be classified into biologically meaningful classes based on physiological processes such as mitochondrial activity, oxidative stress or apoptosis. Since some cells cannot be clearly assigned to a specific class, it may be interesting to use not only supervised but also semi- or unsupervised approaches. All in all, the final goal is an easy-to-use pipeline for single cell segmentation and classification that provides biologically meaningful outputs and visualizations. Artificial Intelligence and Signal and Image Processing Master Thesis

Misestimation of CT-perfusion output in acute stroke due to attenuation curve truncation University of Zurich Bjoern Menze Other organizations: ETH Competence Center - ETH AI Center In this master's thesis project, we are looking for a candidate to apply machine learning techniques to correct and predict signals of incomplete CT perfusion imaging for ischemic stroke. We hope to use machine learning techniques to de-noise and correct for the truncation in CT perfusion signals. In particular, we aim to infer the true attenuation curve after the truncation time-point. Artificial Intelligence and Signal and Image Processing, Central Nervous System, Radiology and Organ Imaging Master Thesis

Developing Deep Learning Algorithms for CT Bone Image Segmentation and Analysis University of Zurich Bjoern Menze Other organizations: ETH Competence Center - ETH AI Center The aim of this project is to use and develop novel machine learning algorithms to segment high-resolution bone images and characterize cortical and trabecular bone structures. To this end, we aim to combine novel ideas tailored to cortical and trabecular bone structures with common segmentation practices and represent the trabecular bone structures as a graph representation with subsequent analysis. Computer Vision, Image Processing Master Thesis

Debris of a feast: Analyzing leftovers after burst of pathogenic bacteria due to phages or predatory bacteria University of Zurich Microbiology Antibiotic resistance is one of the biggest threats to public health and alternatives to conventional antibiotics are urgently needed. An innovative way to kill pathogenic bacteria is to use their natural enemies bacteriophages or a periplasmic predatory bacterium (Bdellovibrio bacteriovorus). While the detrimental destruction of pathogenic bacteria can be an advantage to stimulate the immune response of the infected eukaryote, too much immune stimulation may lead to toxic shock. Bacteriology, Infectious Agents Master Thesis

Spin diffusion in nanoparticles ETH Zurich Cardiovascular Magnetic Resonance The aim of this project is to study the nuclear spin diffusion in semiconducting nanoparticles as a potential next generation hyperpolarized imaging agent Biomaterials, Biosensor Technologies, Condensed Matter Physics-Electronic and Magnetic Properties; Superconductivity, Condensed Matter Physics-Structural Properties, Heat and Mass Transfer Operations, Nanotechnology, Physical Chemistry Bachelor Thesis, Master Thesis, Semester Project

Master's thesis: Optimising a sensor of cellular age University of Zurich Laboratory of Neural Plasticity Ageing is commonly defined as the time-dependent deterioration in organismal fitness. However, variability in ageing kinetics has led to the hypothesis that each cell type possesses its own ageing trajectory. We currently develop novel ageing sensors to characterize the progression of cellular age across different murine cell types and tissues. The aim of the project is to modify the existing sensors allowing for real-time analysis of cellular age in diverse cell types. Biochemistry and Cell Biology, Genetic Engineering and Enzyme Technology, Genetic Immunology, Genetic Technologies: Transformation, Site-directed Mutagenesis, etc., Genome Structure Course Project, Internship, Lab Practice, Master Thesis

Physics Simulator for Optimized MRI of Metal Implants ETH Zurich Cardiovascular Magnetic Resonance Magnetic resonance imaging (MRI) of patients with metallic implants is challenging due to local metal-induced field disturbances, which translate into significant image artifacts. The primary objectives of this research project are to accelerate existing acquisition approaches and to improve image quality by optimizing MR sequence parameters. Biomedical Engineering Master Thesis

Phased array radio localization for behavioral monitoring of birds ETH Zurich Institute of Neuroinformatics Evaluate the potential of using radio localization to greatly simplify our behavioral research on songbirds. Apply machine learning on high-quality datasets with ground truth from the videos to establish a new method. Antenna Technology, Behavioural and Cognitive Sciences, Electrical Engineering, Signal Processing, Sociobiology and Behavioural Ecology Bachelor Thesis, Master Thesis, Semester Project