PhD in Physics-informed generative AI for dynamic cardiac MRI

Job description
Do you want to make cardiac MRI safer for patients and the environment? Join PRIMA-CMR, a EuroTech Universities Alliance project between TU/e and the Technical University of Munich (TUM) and help to develop trustworthy generative AI that makes it possible to scan with far less contrast agent.
Information
Cardiac MRI gives unmatched insight into heart disease, but some of its most important exams depend on gadolinium-based contrast agents. Gadolinium can build up in the body, cannot be given to all patients, and is now an environmental pollutant. A large reduction in the dose would make cardiac MRI safer and available to more patients. At low doses, however, the images no longer contain enough signal for a reliable diagnosis. Current AI methods can make images that look realistic, but they cannot guarantee that what they show is real.
We are looking for a motivated PhD candidate to develop new generative AI methods that recover diagnostic information from low-dose cardiac MRI in a reliable and trustworthy way. Your work will combine knowledge of MR imaging physics, patient-specific clinical information, and uncertainty quantification. The aim is a model whose outputs clinicians can trust, and which tells them when it is not certain. You will validate the methods on real clinical data together with our clinical partners.
This PhD position is part of PRIMA-CMR, a collaborative project within the EuroTech Universities Alliance programme. You will be based at TU/e and spend six months at TUM in the group of prof. Julia Schnabel, which is a world leader in generative and multimodal AI. You will also work with international clinical and academic partners across Europe. This position is embedded in the Medical Image Analysis group, Department of Biomedical Engineering, TU/e. You will be supervised by dr. Cian Scannell, and co-supervised by prof. Julia Schnabel (TUM).

Requirements
A master’s degree (or an equivalent university degree) in Biomedical Engineering, Computer Science, Electrical Engineering, Applied Physics, Applied Mathematics, or a related field.
Background in deep learning, with an interest in generative models.
Knowledge of MRI physics, physics-based modeling, or inverse problems is a plus.
Proficiency in Python and a deep learning framework such as PyTorch.
A research-oriented attitude, analytical thinking, and problem-solving skills.
Ability to work in an international, interdisciplinary team, and willingness to spend six months at TUM in Munich.
Strong interest in medical imaging and cardiovascular disease.
Motivated to develop your teaching skills and coach students.
Fluent in spoken and written English (C1 level).

Conditions of employment
Fixed-term
contract
: 4 years.
A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station.
In addition, we offer you:
Full-time employment for four years, with an intermediate assessment after nine months. You will spend a minimum of 10% of your four-year employment on teaching tasks, with a maximum of 15% per year of your employment.
Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the
Collective Labour Agreement for Dutch Universities
, scale P (min. € 3,204 - max. € 4,051 gross base salary per month (full-time)).
In addition to your base salary, you will receive an 8% holiday allowance and an 8.3% year-end bonus, both calculated based on your annual gross base salary.
Generous leave options: a standard 29 days (based on a 38 hour working week) per year that you can increase to 41 days by working two hours more per week (flexible working hours). This is prorated if you work part-time.
As a TU/e employee, you participate in the ABP pension scheme, providing retirement pension and pension benefits for surviving dependents and occupational disability. TU/e pays 70% of the pension premium, while employees contribute the remaining 30%.
High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own
learning process
.
An excellent technical infrastructure, and on-campus children's day care.
Unlimited access to the modern on‑campus
TU/e Student Sports Center
at an exceptionally affordable rate.
We support your wellbeing with free 24/7 access to OpenUp, providing you and your family with mental health support, expert guidance, and online training.
An allowance for commuting, working from home and internet costs.
A
Staff Immigration Team
and a tax compensation scheme (the
Expat Scheme
) for international candidates.
On our website you can discover even more information about our

conditions of employment
. Build on your career at TU/e!

Employer
TU/e
We are a leading international university where scientific curiosity meets a hands-on mindset. We work in an open and collaborative way with high-tech industries to tackle complex societal challenges. Our responsible and respectful approach ensures impact — today and in the future. TU/e is home to over 13,000 students and more than 7,000 staff, forming a diverse and vibrant academic community. Discover what
Light Up
means to TU/e in our short
company video
.
Our university is located in Brainport Eindhoven — a world‑leading tech region with more than 7,000 high‑tech companies and strong R&D activity. Known for breakthroughs in AI, photonics, semiconductors and advanced manufacturing, Brainport is a place where technology serves people and society. Learn more about the Brainport region
here
.
The Department of Biomedical Engineering offers top-level education and research in one of the most relevant and exciting scientific disciplines of the 21st century: engineering health. In combining engineering and life sciences, through challenge-based learning and a multidisciplinary approach in collaboration with hospitals, industry and others, the department addresses the great challenges of the future, striving to improve healthcare and society as a whole.