Postdoctoral Research Associate in AI

University-of-Oxford; Oxford; Híbrido; GBP 40.000 - 49.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Hybrid working

Interdisciplinary team

Silver Athena SWAN

Descripción de la vacante

University of Oxford invites applications for two Postdoctoral Research Associate positions in AI, based in the Kavli Institute for Nanoscience Discovery, Oxford. The roles involve leading one of two research strands on biological data and AI generalisation and controls, with a focus on data governance and reproducible computational research.

Successful candidates will work in an interdisciplinary team, contribute to publications, and engage in scientific presentations.

Formación

• PhD in ML/CS/Statistics/Computational Science or related field is required or near completion.
• Experience designing and conducting independent empirical computational research.
• Strong Python programming skills and experience with training/evaluating ML models.
• Understanding of computational techniques used in generative or engineering biology.

Responsabilidades

• Design and conduct reproducible computational experiments to study data governance and AI capabilities.
• Analyse results, develop methods, and contribute to scientific publications/presentations.
• Collaborate within an interdisciplinary research environment and manage independent research activities.

Conocimientos

Python programming

Empirical computational research

Data governance understanding

Statistical analysis

Experimental design

Educación

PhD in Machine Learning, Computer Science, Statistics, Computational Science or related area

Close to completion before appointment

Herramientas

Python

Descripción del empleo

UK date and time: 02-November-2026 01:35

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.

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.

Salary (£) : £40,178 - £48,700 per annum (inclusive of Oxford University Weighting)