This PhD project at Kingston University focuses on advancing Medical Image Analysis using Artificial Intelligence. The research aims to develop innovative computer vision and AI tools that automate the analysis of medical images from modalities such as MRI, CT, and ultrasonography. By integrating additional health-related data—including clinician reports, genomics, and longitudinal studies—the project seeks to enhance the accuracy and utility of AI systems in clinical decision support.

Supervised by Professor Dimitrios Makris, an expert in computer vision and medical imaging, the successful candidate will join the Faculty of Engineering, Computing and the Environment. The research group is active in areas such as deep learning, radiomics, and software engineering, with recent publications addressing optimization of deep learning models for low-resource environments and efficient classification of glioblastoma sub-regions.

Applicants should possess a first or upper second class honours degree, and preferably an MSc, in Computer Science, Engineering, Mathematics, Physics, or a closely related discipline. Strong programming skills and a keen interest in computer vision and medical image analysis are essential. The project is part of the Graduate School studentships competition for October 2026 entry, offering funding opportunities that may include tuition and stipend. For full details, candidates should consult the Kingston University PhD Studentships page and the Faculty research website.

The application deadline is March 4, 2026. Prospective students are encouraged to contact Prof Dimitrios Makris ([email protected]) for informal discussions about the project. To apply, review the studentships information and follow the instructions provided on the university and faculty research pages.

This opportunity is ideal for candidates seeking to become experts in medical image analysis, artificial intelligence, and computer vision, and who wish to contribute to impactful research supporting clinicians in healthcare decision-making.