PhD Candidate in Explainable AI and Foundation Models for CT Imaging

Official university vacancy imported from Maastricht University. Details are presented for discovery and may change. Confirm the latest requirements and apply on the official source.

• Our goal: To develop novel AI methods for explainability in medical imaging, including diffusion-model-based approaches, and to advance foundation models for CT through model development, training, evaluation, and external validation.
• Your colleagues: You will join the Department of Precision Medicine at Maastricht University, embedded within GROW and the Faculty of Health, Medicine and Life Sciences. You will work in an interdisciplinary environment connecting artificial intelligence, medical imaging, and clinical translation.
• You hold, or will shortly obtain, a Master’s degree in Artificial Intelligence, Computer Science, Biomedical Engineering, Medical Image Analysis, Applied Mathematics, Data Science, or a closely related field.
• You have a solid theoretical and practical background in machine learning and deep learning, including experience developing, training and evaluating models, preferably for image analysis tasks.
• You have strong programming skills in Python, including the ability to develop and adapt code for machine-learning experiments, train and evaluate deep-learning models, and process and analyse experimental results. You have hands-on experience using a deep-learning framework, preferably PyTorch to develop and adapt code, train deep-learning models, and evaluate their performance.
• You have knowledge of, or a strong interest in, the principles of generative modelling and an interest in applying and further developing generative approaches, including diffusion models, for medical imaging.
• You have a C1 level of proficiency in written and spoken English, according to the Common European Framework of Reference for Languages (CEFR).
• Experience with explainable AI, uncertainty estimation, trustworthy AI, or model interpretability.
• Experience with generative models, particularly diffusion models.
• Experience with foundation models, self-supervised learning, representation learning, or large-scale pretraining.
• Experience with medical imaging, particularly CT, and associated image formats or processing workflows.
• Familiarity with DICOM, NIfTI, image registration, segmentation, or radiological image-analysis pipelines.
• Experience with high-performance computing, distributed training, or working with large imaging datasets.
• Experience evaluating models on heterogeneous or multi-centre data.
• A master’s thesis, publication, research internship, or open-source project relevant to the position.
• A 12-month contract (1,0 FTE) with the prospect of a 3 year extension based on mutual satisfaction.
• A gross monthly salary between € 3.204 and € 4.051 (based on full-time employment of 38 hours per week). 8% holiday allowance and an 8.3% year-end bonus.
• 29 vacation days (based on full-time), four additional days off (Carnival Monday and Tuesday, Good Friday, and Liberation Day), and the possibility to accrue up to 12 extra days through compensation hours.
• Freedom and space to shape your work independently and develop your ideas.
• A close-knit community of colleagues to collaborate and grow with.
• A solid pension plan via ABP, company fitness schemes, and access to various university sports facilities.
• Access to doctoral training, scientific conferences, and opportunities to develop specialist expertise in AI and medical imaging.
• Access to a strong interdisciplinary network in artificial intelligence, imaging, and precision medicine.
• An inspiring work environment in the heart of Europe.
• A motivation letter explaining your interest in the project and your relevant background.
• Your curriculum vitae.
• Copies of your bachelor’s and master’s degree transcripts.
• Contact details of two referees.
• Where applicable, links to relevant publications, your master’s thesis, code repositories, or other research outputs.