PhD candidate 'Clinical and technical validation of Generative AI-based Digital Twins for cardiovascular imaging'

You will be based in the Department of Medical Imaging at Radboud University Medical Center (Radboudumc), embedded in the Diagnostic Image Analysis Group (DIAG). DIAG is one of Europe's leading research groups in medical image analysis, with researchers across Radiology and Nuclear Medicine, Pathology, and Cardiology. The working atmosphere is collaborative, international, and impact & research driven.

This project represents a novel dimension for the group: a bridge toward rigorous real-world clinical evaluation of AI. Radboudumc operates the Grand Challenge platform— a globally used infrastructure for benchmarking medical imaging AI — which you will work with directly. The group has a strong track record of publishing in high-impact journals.

You will be (hybridly but personally) supervised by Dr M. Huisman (PI), attending cardiovascular and musculoskeletal radiologist and Vice President Elect of EuSoMII, with expertise in clinical AI validation, post-market surveillance, and standardization of AI as a regulated clinical technology. Your promotor is Prof. Henkjan Huisman (DIAG, Radiology). You will work relatively independently, embedded within a group with strong technical expertise in imaging AI and have regular interaction with international partners across the TWIN-X consortium. Hybrid working is an option, depending on individual factors and project requirements.

At the department of Medical Imaging, you work on diagnostics, interventions, education, and scientific research. Our focus spans three key areas: Radiology, Nuclear Medicine, and Anatomy. Together with your colleagues, you contribute to the early detection of diseases and the improvement of treatments, helping us make healthcare more precise and patient friendly.

The ideal candidate is an intellectually independent, highly intrinsically motivated researcher with a strong interest in bridging AI development and clinical evidence generation. Success in this position requires the ability to translate technical AI methods into clinically meaningful evaluation frameworks, while working effectively in a multidisciplinary and international environment. A flexible, resourceful, and proactive mindset is essential, with strong ownership of work, the ability to structure and drive projects independently, and to thrive in a setting with high autonomy and trust.

Education

You have a MSc degree in a relevant field such as Technical Medicine, Biomedical Engineering, Medical Image Analysis, Medical Informatics, Statistics, Computer Science, or a related discipline.

Experience & competencies

• High AI literacy: you follow current developments in large language models, foundation models, and agentic AI; you can place new techniques in context and critically evaluate their relevance.
• Experience running or evaluating AI models, working with LLM APIs, or building (agentic) pipelines.
• Experience with medical imaging data formats, reader studies, NLP or text extraction pipelines, or evaluation platforms such as Grand Challenge is an advantage.
• Programming experience in any modern language or tool.
• A basic understanding of clinical research methodology; deep clinical expertise is not required and will be provided through supervision.
• Scientific writing and presenting skills.
• Working proficiency in English (C1-C2).

• This is a 4-year PhD position within the TWIN-X Horizon Europe consortium (HORIZON-HLTH-2025-01-TOOL-03)
• This position requires a Certificate of Conduct (VOG).

We are recruiting for this position ourselves. Unsolicited marketing is not appreciated, but do feel free to share the vacancy in your network!