> Markdown version of [/jobs/ext/4093440-postdoctoral-researcher-in-advanced-image-analysis-and-machine-learning-for-brain-imaging](https://www.wearedevelopers.com/jobs/ext/4093440-postdoctoral-researcher-in-advanced-image-analysis-and-machine-learning-for-brain-imaging). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Postdoctoral Researcher in advanced image analysis and machine learning for brain imaging - **Company:** University of Oxford - **Location:** Oxford, UK - **Contract:** Temporary contract - **Skills:** Artificial Intelligence, Big Data, C++ (Programming Language), Computer Programming, Image Analysis, Python (Programming Language), Machine Learning, Information Technology - **Published:** October 5, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=7b5caea60cdfa1ac ## About the Role Successful applicants will also have the opportunity to contribute to the next generation of FSL tools, which is the most widely used software toolbox that combines structural, functional, and diffusion MRI analysis. FSL is used in more than 1000 universities and hospitals worldwide (160,000 downloads in the past 5 years). We are looking for two postdocs with advanced experience in both AI (machine learning) and image analysis, including both new methods development and software. Applicants will be expected to hold a BSc (or equivalent) degree in a relevant area (i.e. Physics, Engineering, Computer Sciences, Neurosciences), and a PhD in a relevant computational domain. Also deep knowledge of statistics and Bayesian learning, and necessary mathematical and computational skills to put these into practice. Skills in computer programming (Python or C++) are essential. ## Description A major goal of our group is to extend the reach of imaging neuroscience to take full advantage of the richness of the new big data imaging resources, such as: major advances in the quality of data; complementarity of multi-modal imaging; large subject numbers; and linked information about health outcomes, genetics, and risk factors. This requires new research across multiple domains, including: integrated cross-modal analysis, particularized biologically-interpretable markers, machine learning (advanced AI methods), and modelling of variation in populations to the characterisation of individual subjects.