Postdoctoral Researcher in Computer Vision for Sport Medicine

We are seeking

to appoint a highly motivated Postdoctoral Researcher, to work within the Podium Institute for Sports Medicine and Technology at the University of Oxford. Established in October 2022 the Podium Institute sits within the Institute of Biomedical Engineering (IBME) in the University’s Department of Engineering Science. It constitutes a world-unique ecosystem within which to develop and validate new technologies for the diagnosis, prevention, and management of sport injuries, with emphasis on safety rather than performance in the community, youth (11-18 years of age) and women’s sport. This full-time post is initially fixed-term for 18 months with possible extension. You will work at the intersection of computer vision, artificial intelligence and sports medicine, collaborating closely with researchers within The Podium Institute, the Visual Geometry Group (VGG), the wider Department of Engineering Science, and clinical and biomedical departments across the University. You will join a highly interdisciplinary team of computer vision, machine learning, engineering, neuroscience, clinical and sports medicine researchers working together to develop innovative technologies for understanding athlete movement, identifying injury mechanisms, assessing injury risk and supporting evidence-based injury prevention strategies. A particular focus of the role is the 3D reconstruction of athletes and their interactions from monocular, multi-view and broadcast video, including in contact sports such as rugby, where dense contact, occlusion and close interaction between players make injury mechanisms difficult to capture. You should hold, or be near completion of, a PhD/DPhil in Computer Vision, Machine Learning, Artificial Intelligence, Engineering Science, Computer Science, Robotics, Biomedical Engineering, or a related discipline. You should have strong programming skills in relevant scientific computing frameworks and languages (e.g. Python, PyTorch, TensorFlow or equivalent), and have experience developing, implementing and evaluating machine learning or computer vision models using real-world image and video datasets, including in-the-wild or sports footage. For more information about working at the Department, see Only online applications received before midday on the 26th October 2026 can be considered.