We are seeking
a full-time Senior Research Associate in Cerebrovascular Imaging to join the research group of Prof Thomas Okell at the Podium Institute for Sports Medicine and Technology within the Department of Engineering Science (based in Headington, Oxford). The post is funded by Podium Analytics and is initially fixed-term to 30 September 2027, with possible extension subject to funding. You will be responsible for the development of novel acquisition, reconstruction, image analysis and/or modelling methods for cerebrovascular magnetic resonance imaging (MRI) to improve the assessment of mental health, cognition and brain injury, with applications to athletes and sports-related injuries. This includes contributing to one or more projects that include: 1) leveraging large scale imaging datasets and advanced machine learning approaches to identify novel imaging markers of mental health disorders and cognitive function; 2) developing robust MRI-based acquisition, image reconstruction and physiological modelling approaches that are highly sensitive to cerebrovascular changes; 3) exploring the potential for imaging markers to improve prognosis and treatment stratification for those suffering from chronic pain or mental health conditions; 4) investigating novel MRI approaches to assess microvascular damage and blood-brain barrier disruption; and 5) utilising highly efficient methods for simultaneous imaging of blood vessels, tissue perfusion and brain anatomy to assess cerebrovascular changes. The precise areas of work will depend on the priorities of the group at the start of the post and your skills and experience. You should possess a PhD in a relevant field (e.g. engineering, mathematics, physics or other relevant discipline) and have post-qualification research experience. Previous experience with MRI acquisition, image reconstruction, signal modelling and/or image analysis is essential, and further experience in one or more of the following would be advantageous: MRI pulse sequence programming; running an imaging study; blood-brain barrier evaluation; and/or the application of machine-learning methods to large (ideally imaging) datasets. For more information about working at the Department, see. Only online applications received before midday on 23 March 2026 can be considered.