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PhD in Physics-informed generative AI for dynamic cardiac MRI
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Source: Headcount Labs · original listing ↗
Job data: Headcount Labs, ODbL 1.0. Job descriptions belong to their employers.
Posted Oct 20
About this role
Introduction 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. Job Description 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). Job 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 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! About us 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. Information Do you recognize yourself in this profile and would you like to know more? Please contact the hiring manager Cian Scannell, Assistant Professor of Medical Image Analysis, c.m.scannell@tue.nl. Visit our website for more information about the application process. You can also contact HRServices.bme@tue.nl. Curious to hear more about what it’s like as a PhD candidate at TU/e? Please view the video. Are you inspired and would like to know more about working at TU/e? Please visit our career page. Application We invite you to submit a complete application by using the apply button. The application should include a: - Cover letter in which you describe your motivation and qualifications for the position. - Curriculum vitae, including a list of your publications and the contact information of three references. Kindly note that we may reach out to references at any stage of the recruitment process. We recommend notifying your references upon submitting your application. - Brief description of your MSc thesis (topic, method, contribution) and other related research projects that you conducted. - List of Master's and Bachelor’s courses, including grades. Ensure that you submit all the requested application documents. We give priority to complete applications. We look forward to receiving your application and will screen it as soon as possible. The vacancy will remain open until the position is filled. Please note - You can apply online. We will not process applications sent by email and/or post. - A pre-employment screening (e.g. knowledge security check) can be part of the selection procedure. For more information on the knowledge security check, please consult the National Knowledge Security Guidelines. - Please do not contact us for unsolicited services.