Eindhoven University of Technology (TU/e), in collaboration with the Technical University of Munich (TUM) under the EuroTech Universities Alliance, is offering a fully funded 4-year PhD position in Physics-Informed Generative AI for Dynamic Cardiac MRI. The selected candidate will join the PRIMA-CMR project to develop trustworthy generative AI architectures that enable diagnostic-quality cardiac MRI with significantly reduced gadolinium contrast agent doses.
Overview
| Feature | Details |
|---|---|
| Designation | PhD Candidate (Scientific Staff) |
| Host Institution | Eindhoven University of Technology (TU/e) |
| Partner Institution | Technical University of Munich (TUM) |
| Department | Department of Biomedical Engineering (Medical Image Analysis Group) |
| Research Area | Physics-Informed Generative AI, Cardiac MRI, Deep Learning |
| Location | Eindhoven, Netherlands (with 6 months in Munich, Germany) |
| Employment Type | Full-time (1.0 FTE, 4 years) |
| Salary | €3,204 – €4,051 gross base salary per month (plus allowances) |
| Last Date for Apply | 21-11-2026 |
Research Area
This research sits at the intersection of medical image analysis, deep learning, and magnetic resonance imaging (MRI) physics. The primary focus is developing novel, physics-informed generative AI methods capable of recovering vital diagnostic information from low-dose dynamic cardiac MRI. The project emphasizes clinical trustworthiness, combining patient-specific clinical priors, MRI physics principles, and rigorous uncertainty quantification.
Location
The position is primarily based at the Department of Biomedical Engineering, Eindhoven University of Technology (TU/e), located in Brainport Eindhoven, Netherlands. As part of the EuroTech Universities Alliance, the candidate will also spend six months conducting research at the Technical University of Munich (TUM) in Munich, Germany, within the group of Prof. Julia Schnabel.
Eligibility / Qualification
Candidates must meet the following criteria:
- A Master’s degree (or equivalent) in Biomedical Engineering, Computer Science, Electrical Engineering, Applied Physics, Applied Mathematics, or a related discipline.
- Strong background in deep learning, with demonstrated interest in generative modeling.
- Proficiency in Python and deep learning libraries such as PyTorch.
- Prior exposure to MRI physics, physics-based modeling, or inverse problems is considered a strong plus.
- Analytical mindset, problem-solving orientation, and research-driven attitude.
- Eagerness to collaborate in an international, multidisciplinary team and willingness to complete a 6-month stay at TUM in Munich.
- Enthusiasm for academic teaching and student mentoring (allocating 10–15% time to teaching-related tasks).
- High level of English proficiency (written and spoken, C1 level).
Scholarship Description & Benefits
The PhD position offers full-time university employment for a duration of four years (subject to an intermediate evaluation after nine months) governed by the Collective Labour Agreement for Dutch Universities.
- Monthly Salary: Starts at €3,204 gross per month in the first year, progressing to €4,051 gross per month in the fourth year (Scale P).
- Allowances & Bonuses: An additional 8% holiday allowance and an 8.3% end-of-year bonus based on annual gross earnings.
- Leave: Generous leave starting at 29 days annually (based on a 38-hour workweek), extendable up to 41 days with flexible working arrangements.
- Pension & Healthcare: Participation in the ABP pension scheme (70% employer contribution) along with free 24/7 mental wellbeing support (OpenUp).
- Expat Support: Specialized relocation guidance by the TU/e Staff Immigration Team and eligibility evaluation for the Dutch Expat Scheme (tax compensation).
How to Apply
Applications must be submitted electronically through the official TU/e portal. Submissions sent via email or postal mail will not be processed. Complete application packages must contain:
- A detailed cover letter outlining motivation, research background, and qualifications.
- A comprehensive Curriculum Vitae (CV) with publication history and contact details of three academic references.
- A brief summary of your Master’s thesis (topic, method, contributions) and other related research undertakings.
- Academic transcripts and grade lists for both Bachelor’s and Master’s degrees.
Please submit your application online using the official link:
Apply for PhD in Physics-informed generative AI for dynamic cardiac MRI
Last Date for Apply
The application deadline is 21 November 2026. Applications are screened on a rolling basis, and the position will close once a suitable candidate is appointed.
