PhD Position in Physics-Informed Machine Learning for Cardiac Magnetic Resonance
PhD Position in Physics-Informed Machine Learning for Cardiac Magnetic Resonance
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.
Project background
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.
* 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 holda 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
In line with our values, ETH Zurich encourages an inclusive culture. We promote equality of opportunity, value diversity and nurture a working and learning environment in which the rights and dignity of all our staff and students are respected. Visit our Equal Opportunities and Diversity website to find out how we ensure a fair and open environment that allows everyone to grow and flourish. Sustainability is a core value for us – we are consistently working towards a climate-neutral future .
Curious? So are we.
We look forward to receiving your online application including:
* Detailed CV
* Study subjects including grades and
* Contact information of two referees
Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.
For further information about the position and the group please contact Prof Dr Sebastian Kozerke by e-mail: kozerke@biomed.ee.ethz.ch (no applications) or visit our website .
ETH Zurich is one of the world’s leading universities specialising inscience and technology. We are renowned for our excellent education,cutting-edge fundamental research and direct transfer of new knowledgeinto society. Over 30,000 people from more than 120 countries find ouruniversity to be a place that promotes independent thinking and anenvironment that inspires excellence. Located in the heart of Europe,yet forging connections all over the world, we work together todevelop solutions for the global challenges of today and tomorrow.
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