Kavli Institute for Nanoscience Discovery, New Biochemistry Building, South Parks Road, Oxford, OX1 3QU
Applications are invited for two Postdoctoral Research Associate positions in AI to join the research group of Dr Oliver Crook with the Department of Chemistry at the University of Oxford, based at the Kavli Institute for Nanoscience Discovery.
About the role
The research programme aims to investigate how access to biological training data affects artificial intelligence (AI) capabilities and to generate empirical evidence that can inform approaches to data governance. The two positions form part of a single programme, with each postholder leading one of two complementary research strands and working closely with the other postdoctoral researcher.
The first research strand, Biological Data and AI Generalisation, will investigate how the composition, diversity and annotation of biological training datasets influence model performance and generalisation. The successful candidate will design and undertake controlled computational experiments to identify which dataset properties contribute to model capabilities and distinguish genuine generalisation from memorisation, data leakage and similarities between training and test data.
The second research strand, Evaluating Biological Data Controls, will investigate how restricting or removing selected training data affects AI capabilities and the extent to which these effects persist. The successful candidate will evaluate the robustness and limitations of proposed data controls, including whether their effects persist when models have access to alternative datasets, synthetic data or subsequent adaptation.
Both roles will involve designing and conducting reproducible computational research, analysing results, developing and adapting research methods, and contributing to scientific publications and presentations. You will work collaboratively within an interdisciplinary research environment, helping to develop an evidence base for understanding the relationship between biological data access, AI capabilities and data governance.
Applicants may express an interest in either or both research strands.
These are full-time (37.5 hours per week), fixed-term positions for up to 18 months. Both posts are based in Oxford, although hybrid working may be possible, with weekly on-site attendance required. Applications from candidates wishing to work part-time or flexible hours will also be considered.
About you
You will hold a PhD in Machine Learning, Computer Science, Statistics, Computational Science or a related area (or be close to completion), prior to taking up the appointment.
You will have experience of designing and conducting independent empirical computational research, with strong Python programming skills and practical experience of training, adapting or evaluating machine-learning models. You will also have an understanding of the main computational and experimental techniques used in generative or engineering biology.
You will demonstrate strong quantitative and analytical skills, including experimental design, statistical analysis and the ability to identify confounding factors, develop appropriate controls and interpret uncertain or negative findings.
Excellent communication and organisational skills are essential, together with the ability to manage your own research activities, maintain reproducible computational records and work effectively within an interdisciplinary team.
You will have an interest in biological data governance and in applying technical research to questions about AI capabilities and policy.
Experience with biological sequence data, foundation models, model generalisation, memorisation or data leakage would be advantageous. Experience with data ablation, synthetic data, model robustness, unlearning, training-data attribution or biophysical simulation data would also be beneficial.
Application process
Applications for this vacancy are to be made online. As part of your application, you will be required to upload a supporting statement and CV.
Your supporting statement must explain how you meet each of the selection criteria for the post, using clear examples drawn from skills and experience. Please also indicate whether you are interested in the Biological Data and AI Generalisation strand, the Evaluating Biological Data Controls strand, or both. Please note that certificates, references and research papers should not be provided at this stage.
To apply for this role and for further details, including the job description and selection criteria, please click on the link below.
The closing date for applications is 12.00 midday, Monday 23 November 2026, UK time. Interviews will be held as soon as possible thereafter.
The University of Oxford and The Department of Chemistry are Silver Athena SWAN holders. Applications are particularly welcome from women and black and ethnic minority candidates, who are under-represented in Chemistry research posts in Oxford.
Job details
- Contact Person: HR Team
- Vacancy ID: 189643
- Closing Date & Time: 23-Nov-2026 12:00
- Pay Scale: RESEARCH GRADE 7
- Contact Email: hr@chem.ox.ac.uk
- Salary: £40,178 - £48,700 per annum (inclusive of Oxford University Weighting)
