Postdoctoral Researcher in Computational Neuroanatomy & Artificial Intelligence

The postdoctoral researcher will lead the development of computational methods for aligning cortical organisation across species using transcriptomic and anatomical data combined with modern machine-learning approaches. In particular, the postholder will work on approaches that learn mappings between mouse, marmoset, macaque, and human cortex, and enable the comparison of the macroscopic cortical organisation across species. This will be a key pillar of a larger UKRI-funded research programme developing anatomy-driven artificial intelligence methods for fundamental and translational neuroscience.

Working under the guidance of the Principal Investigator, the postholder will contribute to model development, multimodal data integration, analysis, writing and presentation. They will collaborate closely with other team members, local and international partners (e.g. Prof. Rogier Mars, University of Oxford; Prof. Nicola Palomero-Gallagher, Research Center Jülich, Germany; Prof. Fenna Krienen, Princeton University, USA), and will be supported in developing independence, publications, and career progression.

This role is well suited to a postdoctoral researcher seeking advanced training at the interface of computational neuroscience, neuroanatomy, and AI, with opportunities to build expertise towards future fellowships or faculty positions.

Please see the below 'Job Description' for further details on the role, responsibilities, and selection criteria, as well as further information about the university and how to apply.

This post is full time (part time 30 hours / FTE 0.8 minimum will be considered) and fixed term until 31st March 2029.

Only applications received before midday 12:00 on Friday 27th March 2026 will be considered.

Interviews will be held as soon as possible thereafter.

Contact Email:

Salary (£): £39,424- £47,779 per annum (less experienced candidate may be appointed at Grade 6: £35,681 to £41,636 per annum). Pay Scale: RESEARCH GRADE 7.

Closing Date & Time: 27-Mar-2026 12:00.