The CMR 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
Our research has demonstrated approaches to data- and physics-informed synthesis of medical imaging data allowing to train inference machines and classifyers based on paired ground truth and synthetic imaging data. We capitalize on our previous and current work allowing to not only acquire MR imaging data of cardiac anatomy and function but also information about cardiac micro- and mesostructures derived from diffusion tensor imaging of the heart along with all parameters determining the measurement process itself.
Job description
The position to fill concerns advanced data synthesis (both via machine learning based generative models and physics simulation) and data inference (including segmentation, classification, parameter inference and mesh fitting) based on data-driven and (bio)physics-informed machine learning principles. The project aims at training and learning using both bottom-up and top-down approaches with applications to cardiac image synthesis, reconstruction and classification. The position is embedded in our overall activities of advancing MR methodology as part of improving decision support in cardiovascular patients.
Your profile
You hold a first-class PhD degree in computer science, electrical engineering, biomedical engineering, physics or applied mathematics. You should present with expertise in advanced signal and data processing and its applications to cutting-edge imaging. Excellent programming skills (Matlab/Python, C(++)) and expertise with deep learning frameworks such as PyTorch, TensorFlow, Keras. Further, you would have extensive experience with standard supervised machine learning on image data (classification, segmentation), generative image models (VAEs, GANs, diffusion models), working in the low data regime, working with 3D data (both meshes and voxel arrays), and medical image data (DICOM, NiFTI, PAR/REC).
Ideally, you would also have experience with physic-informed neural networks, and inverse imaging problems. Lastly, previous experience with student supervision is a plus, and an innovative spirit and team player skills round off your profile.
We offer
Available resources include the full range of programmable experimental and clinical MR equipment (0.75, 1.5, 3 and 7T) fully dedicated to research, advanced medical data streaming and processing machines, as well as state-of-the-art local and scalable cloud-based compute infrastructure (CPU, GPU). Long-standing and very successful cooperations with clinical partners (cardiology, radiology) offer opportunities for testing and data collection in real-world applications.
ETH Zurich is one of the world’s leading universities specialising in science and technology. We are renowned for our excellent education, cutting-edge fundamental research and direct transfer of new knowledge into society. Over 30,000 people from more than 120 countries find our university to be a place that promotes independent thinking and an environment that inspires excellence. Located in the heart of Europe, yet forging connections all over the world, we work together to develop solutions for the global challenges of today and tomorrow.
Working, teaching and research at ETH Zurich
Interested?
We look forward to receiving your online application including:
- motivation letter
- detailed CV
- study subjects including grades and
- contact information of two referees
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 websites www.cmr.ethz.ch. Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.
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.
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