PhD Candidate in Explainable and Foundation Models for CT Imaging

• Undertake a four-year doctoral research project leading to a PhD thesis.
• Develop and evaluate new methods for explainable AI in medical imaging, with particular attention to the use of diffusion models for explanation and interpretation.
• Contribute to the design, training, adaptation, and external validation of foundation models for CT images.

What they ask

• An analytical and curious researcher with an interest in technically innovative research at the intersection of artificial intelligence, medical imaging, and clinical translation.
• Enjoys tackling complex problems and working in an interdisciplinary and international environment.
• Takes ownership of their work and develops as an independent researcher.
• Approaches research systematically and has experience developing well-structured, well-documented, and reproducible research code.
• Organizes experiments in a way that enables results to be reproduced and their work to be understood and further developed by others.
• Motivated to further develop as an independent researcher and successfully complete their PhD within the appointment period.
• Holds, 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.
• A solid theoretical and practical background in machine learning and deep learning, including experience developing, training, and evaluating models, preferably for image analysis tasks.
• 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 analyze experimental results.
• Hands-on experience using a deep-learning framework, preferably PyTorch.
• Knowledge of, or a strong interest in, the principles of generative modeling and an interest in applying and further developing generative approaches, including diffusion models, for medical imaging.
• C1 level of proficiency in written and spoken English.

Preferences.

• 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.