PhD Position in Physics-Informed Machine Learning for Cardiac Magnetic Resonance
HeuteAngaben zum Job
| Firma | ETH Zürich | Pensum | 100% |
| Einsatzort | Zurich |
Job-Inhalt
Project background
The CMR Zurich group at the Institute for Biomedical Engineering develops Magnetic Resonance (MR) technology and methods to assess the cardiovascular system. We devise the next generation of diagnostic tools for quantification of blood flow, organ perfusion, metabolism and function, tissue composition, microstructure and mechanics. The group exploits principles from physics, electrical engineering and computer science to design highly efficient and sensitive imaging and inference approaches to help guide diagnosis and treatment in cardiovascular patients.
Cardiovascular Magnetic Resonance (CMR) plays a central role in the non-invasive assessment of cardiac anatomy, function, and blood flow. However, long acquisition, reconstruction and data processing times continue to limit its clinical reach and robustness. Recent advances in machine learning, particularly deep learning and physics-informed methods, offer transformative opportunities to redesign how data are acquired and reconstructed, and how physiological parameters are inferred from the data.
Job description
- This PhD project focuses on developing learning-based strategies for accelerated data acquisition, physics-informed image reconstruction, and quantitative inference of cardiovascular anatomy and hemodynamics.
- The research will integrate modern machine learning with MR signal modeling, computational imaging, and fluidmechanis, with the ultimate goal of enabling faster, more reliable, and more informative CMR.
The project will be carried out in a highly interdisciplinary environment at ETH Zurich, with close collaboration between engineers, physicists, clinicians, and data scientists, and access to state-of-the-art imaging infrastructure and clinical datasets.
Profile
You hold a Master of Science degree with first-class grades in:
- Computer science
- Electrical engineering
- Biomedical engineering
- Physics or
- Applied mathematics
You present with expertise in advanced signal and data processing and its applications to cutting-edge imaging. Developing programming skills (Python, C(++)) and hands-on work with deep learning frameworks such as PyTorch, TensorFlow, Keras have been in your focus. Further, experience with standard supervised machine learning on image data (classification, segmentation), physics-informed neural networks, and having worked with large datasets are assets. An innovative spirit and team player skills round off your profile.
We offer
- We are a dynamic and international team embedded in information technology, electrical engineering and the medical faculty, with a long-standing track record in CMR research
- First-class infrastructure is available, including experimental and clinical MR systems fully dedicated to research, state-of-the-art local and scalable cloud-based compute infrastructure (CPU, GPU) and workshops for mechanical, electrical and electronic development projects
- Long-standing and very successful cooperations with industry and clinical partners (cardiology, radiology) offer opportunities for networking as well as for deployment of research results in real-world applications