Eindhoven University of Technology
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PhD in Physics-informed Generative AI for Dynamic Cardiac MRI Eindhoven University of Technology in Netherlands
Degree Level
PhD
Field of study
Computer Science
Funding
Full-time PhD employment for four years with an intermediate assessment after nine months. Salary is scale P, €3,204–€4,051 gross base salary per month, plus 8% holiday allowance and 8.3% year-end bonus. Benefits include pension, leave, training, commuting/work-from-home/internet allowance, and support for international candidates.
Deadline
Nov 21, 2026
Country
Netherlands
University
Eindhoven University of Technology

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About this position
PhD opening at Eindhoven University of Technology (TU/e) in the Department of Biomedical Engineering / Medical Image Analysis group.
The project is titled Physics-informed generative AI for dynamic cardiac MRI and is part of PRIMA-CMR, a EuroTech Universities Alliance collaboration between TU/e and the Technical University of Munich (TUM). The research aims to make cardiac MRI safer and more accessible by reducing reliance on gadolinium-based contrast agents while preserving diagnostic quality.
You will develop trustworthy generative AI methods for low-dose cardiac MRI, combining medical imaging, MRI physics, deep learning, physics-informed modeling, inverse problems, and uncertainty quantification. The goal is to recover diagnostic information from low-signal images in a way clinicians can trust, including knowing when the model is uncertain. Validation will be done on real clinical data with clinical partners across Europe.
The position is embedded in the Medical Image Analysis group at TU/e and includes a six-month research stay at TUM in Munich in the group of Prof. Julia Schnabel. Supervision is by Dr. Cian Scannell, with co-supervision by Prof. Julia Schnabel.
Eligibility highlights: a master’s degree in Biomedical Engineering, Computer Science, Electrical Engineering, Applied Physics, Applied Mathematics, or a related field; strong background in deep learning and generative models; Python and PyTorch skills; interest in MRI physics and cardiovascular disease; and fluent English (C1). Teaching involvement is expected during the four-year full-time appointment.
Funding and conditions: full-time PhD employment for four years, scale P salary (€3,204–€4,051 gross/month), plus holiday allowance, year-end bonus, pension, leave, training, and other TU/e benefits.
How to apply: submit the application online through the TU/e vacancy portal. Include a cover letter, CV with publications and three references, a short description of your MSc thesis and related projects, and your Bachelor’s/Master’s course list with grades. The vacancy remains open until filled; complete applications are prioritized.
Funding details
Full-time PhD employment for four years with an intermediate assessment after nine months. Salary is scale P, €3,204–€4,051 gross base salary per month, plus 8% holiday allowance and 8.3% year-end bonus. Benefits include pension, leave, training, commuting/work-from-home/internet allowance, and support for international candidates.
What's required
A master’s degree or equivalent in Biomedical Engineering, Computer Science, Electrical Engineering, Applied Physics, Applied Mathematics, or a related field. Applicants should have a background in deep learning with interest in generative models, preferably knowledge of MRI physics, physics-based modeling, or inverse problems, and proficiency in Python and a deep learning framework such as PyTorch. The post also asks for a research-oriented attitude, analytical thinking, problem-solving skills, ability to work in an international interdisciplinary team, willingness to spend six months at TUM in Munich, strong interest in medical imaging and cardiovascular disease, motivation to develop teaching skills and coach students, and fluent spoken and written English at C1 level.
How to apply
Apply online via the apply button on the TU/e vacancy page. Submit a cover letter, CV with publications and three references, a brief description of your MSc thesis and related projects, and a list of Master’s and Bachelor’s courses with grades. Email applications are not accepted.
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