Postdoctoral Research Scientist – AI for Bionanoscience

Department of Physiology, Anatomy and Genetics, Kavli Institute for Nanoscience Discovery, Dorothy Crowfoot Hodgkin Building, South Parks Road, Oxford, OX1 3QU

About us:

The  Kavli Institute for Nanoscience Discovery Kavli Institute for Nanoscience Discovery (Kavli INsD), established in March 2021, brings together over 30 faculty and 450 researchers across disciplines to tackle global health challenges. By fostering interdisciplinary collaboration and providing cutting-edge facilities, it encourages innovation and shared discovery, and benefits from the close proximity of the scientific departments as well as advanced imaging and characterisation facilities and state-of-the-art-instrumentation.

At the  Department of Physiology Anatomy & Genetics (DPAG) we undertake discovery science where we reassemble physiological processes at the molecular, cellular, tissue and systems level of organisation. In so doing we provide a bridge to translational medicine, and interface between physical and life sciences. We are committed not only to innovative research and the highest standard of teaching, but also to creating an inclusive and supportive working environment.

Overview of the role:

We are seeking to appoint two Postdoctoral Research Scientists in AI for Bionanoscience to join Professor Dame Molly Stevens’s lab at the Kavli Institute for Nanoscience Discovery, University of Oxford. We are seeking creative, motivated and collaborative researchers to develop next-generation AI methods for scientific and biomedical discovery.

The posts span two complementary research directions:

• AI for experimental science and multimodal scientific data analysis — developing machine learning methods to support experimental design, interpretation and analysis of complex scientific datasets across biomaterials, biosensing, diagnostics and tissue engineering.
• AI for autonomous molecular and materials discovery — developing predictive, generative and foundation-model-based AI methods for molecular optimisation, biomaterials engineering, protein and binder design, lipid nanoparticle formulation and materials discovery.

Successful candidates will contribute to one/both areas depending on expertise/interests.

Key responsibilities:

• Develop and apply AI and machine learning methods to challenges in biomaterials, molecular discovery and experimental science.
• Build reproducible workflows for analysing complex multimodal datasets.
• Collaborate with interdisciplinary teams to define problems, analyse data and translate outputs into meaningful scientific insights.
• Apply advanced machine learning techniques, including supervised, self-supervised, generative and active learning methods.

Selection criteria:

• PhD/DPhil (or near completion) in a computational discipline (e.g. AI, computer science, data science) or a scientific field with strong computational expertise.
• Strong knowledge of machine learning, statistical modelling and scientific computing.
• Proficiency in Python and frameworks such as PyTorch, TensorFlow, JAX, or scikit-learn.
• Experience analysing complex datasets, ideally in biomolecular design, materials discovery or related areas.

Please refer to the Job Description for a full list of responsibilities and criteria.  

What we offer:

We provide a supportive working environment and comprehensive benefits:

• Contributory pension scheme
• 38 days annual leave
• Childcare support and family leave
• Cycle and electric car schemes
• Employee Assistance Programme
• Social and sports club membership
• Discounted travel schemes

We welcome applications from individuals who wish to be considered for part-time working/other flexible arrangements. 

How to apply:

Please submit a CV, supporting statement addressing the selection criteria.

Closing date: 12 noon, 01 July 2026

Interviews: Week commencing 20 July 2026 (via Microsoft Teams)

Applications are particularly welcome from women, black and minority ethnic candidates who are under-represented in academic posts in Oxford. 

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