 University of Zurich| Acronym | UZH | | Homepage | http://www.uzh.ch/ | | Country | Switzerland | | ZIP, City | | | Address | | | Phone | | | Type | Academy | | Current organization | University of Zurich | | Child organizations | | | Members | | | Memberships | |
Open OpportunitiesThe aim of this project is to implement and test different T1ρ measurement approaches using MR simulations and measurements. - Biomedical Engineering, Medical Physics
- Bachelor Thesis, Semester Project
| Our lab is looking for a highly motivated Master student (6-12 months), who is interested to conduct a research in the field of disease modeling using ADHD patient-specific cells. The process will involve learning how to analyze preexisting data from single cell RNA sequencing (scRNAseq) generated from neural stem cells (NSCs) and forebrain cortical neurons (FCNs) derived from human induced pluripotent stem cells (iPSCs), by using bioinformatics. These cell lines were previously generated in our lab and are derived from patients with Attention-Deficit Hyperactivity Disorder (ADHD). We will investigate the transcriptome profile of both NSCs and FCNs, which will include analyzing the Wnt signaling pathway. This pathway plays a crucial role in coordinating important cellular events during neurodevelopment. Moreover, the student will learn how to scientifically interpret and discuss scientific papers and conduct research with independence and critical thinking. - Cell Development (incl. Cell Division and Apoptosis), Computational Biology and Bioinformatics, Neurosciences, Psychiatry, Quantitative Genetics
- 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.
** images from:
- Rodríguez‐Cantano, Rocìo et al International journal for numerical methods in biomedical engineering 35.5 (2019): e3178. https://doi.org/10.1002/cnm.3178
- Teh, Irvin et al. Journal of Cardiovascular Magnetic Resonance 19 (2017): 1-14. https://doi.org/10.1186/s12968-017-0342-x - Biomedical Engineering
- Bachelor Thesis, Semester Project
| Our lab is looking for a highly motivated Master student (6-12 months), who is interested to conduct a research in the field of disease modeling using ADHD patient-specific cells. The process will involve learning how to generate and culture human induced pluripotent stem cells (iPSCs) and generation of neural stem cells (NSCs) from patients with Attention-Deficit Hyperactivity Disorder (ADHD), as well as how to scientifically interpret and discuss scientific papers and conduct research with independence and critical thinking. - Biochemistry and Cell Biology, Neurosciences, Pharmacology and Pharmaceutical Sciences
- Master Thesis
| Our lab is looking for a highly motivated Master student (6-12 months), who is interested to conduct a research in the field of disease modeling using ADHD patient-specific cells. The process will involve learning how to generate and culture human induced pluripotent stem cells (iPSCs) and generation of Neural Stem Cells (NSCs) and Forebrain Cortical Neurons (FCNs) from patients with Attention-Deficit Hyperactivity Disorder (ADHD), as well as how to scientifically interpret and discuss scientific papers and conduct research with independence and critical thinking. - Biochemistry and Cell Biology, Neurosciences, Pharmacology and Pharmaceutical Sciences
- Master Thesis
| In this project, the student applies concepts from current advances in image generation to create artificial events from standard frames. Multiple state-of-the-art deep learning methods will be explored in the scope of this project. - Computer Vision
- Master Thesis, Semester Project
| The project aims to develop a data-driven keypoint extractor, which computes interest points for event camera data. Based on a previous student project (submitted to CVPR23), the approach will leverage neural network architectures to extract and describe keypoints in an event stream. - Computer Vision
- Master Thesis, Semester Project
| The aim of the project is to generate synthetic LGE CMR images from ground truth segmentation masks using a SPADE-based GAN. - Biomedical Engineering, Computer Vision, Image Processing, Neural Networks, Genetic Alogrithms and Fuzzy Logic
- Bachelor Thesis, Master Thesis, Semester Project
| In Flow MRI, image artifacts mainly result from cardiac and respiratory motion, causing blurring or ghosting. CINE imaging addresses cardiac motion by acquiring data throughout the cardiac cycle. To tackle respiratory motion, traditional methods involved measuring respiratory signals and accepting data within a limited respiratory motion range, at the cost of reduced scan efficiency and increased acquisition time. Newer approaches record data in a free breathing manner and use self-navigation to organize it into bins, improving efficiency and reducing acquisition time.
Low rank priors are a cutting-edge technique in dynamic MR image reconstruction, and recent research by Hoh et al. has shown that incorporating motion information into locally low rank (LLR) reconstruction (MI-LLR) between bins can improve reconstructions for free breathing 3D cardiac perfusion MRI.
The aim of this project is to investigate the benefit of using MI-LLR reconstructions on Flow data.
- Engineering and Technology, Information, Computing and Communication Sciences, Mathematical Sciences, Medical and Health Sciences, Physics
- Bachelor Thesis, Master Thesis, Semester Project
| In this project, the student extends upon a previous student project (published at ECCV22) and current advances from the UDA literature in order to transfer multiple tasks from frames to events. The approach should be validated on several tasks in challenging environments (night, high-dynamic scenes) to highlight the benefits of event cameras. - Computer Vision
- Master Thesis, Semester Project
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