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.
Contract & job type
Full‑time, Fixed‑term for 18 months.
About us
The Kavli Institute for Nanoscience Discovery (Kavli INsD), established in March 2021, brings together over 30 faculty and 450 researchers from diverse disciplines to tackle global health challenges. By fostering interdisciplinary collaboration and providing cutting‑edge facilities, it encourages innovation and shared discovery, benefiting from close proximity to scientific departments and advanced imaging, characterisation, and state‑of‑the‑art instrumentation.
At the Department of Physiology, Anatomy & Genetics (DPAG) we conduct discovery science that reassembles physiological processes at the molecular, cellular, tissue and systems levels. We provide a bridge to translational medicine and an interface between physical and life sciences, committed to innovative research, high‑standard teaching, and an inclusive, supportive working environment.
Overview of the role
We are seeking two Postdoctoral Research Scientists in AI for Bionanoscience to join Professor Dame Molly Stevens’s lab. Candidates will develop next‑generation AI methods for scientific and biomedical discovery. The posts cover two complementary 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 or both research areas, depending on expertise and interests. The role is highly multidisciplinary and collaborative, requiring close interaction with experimental and computational researchers and the potential to deliver AI systems that advance scientific discovery in therapeutics and disease diagnostics.
Key responsibilities
- Develop, adapt and apply machine learning and AI methods to scientific problems in biomaterials, molecular and materials discovery, and data‑driven experimental science.
- Build robust and reproducible computational workflows for multimodal scientific data analysis, model development, and validation.
- Collaborate with experimental and computational researchers to define tractable machine learning problems, analyse complex multimodal datasets and translate model outputs into scientifically meaningful insights.
- Develop or adapt machine learning methods, including supervised, self‑supervised, generative and active learning approaches, for scientific applications.
Selection criteria
- Hold, or be close to completing, a PhD/DPhil in a computational discipline (e.g. machine learning, computer science, computational chemistry, applied mathematics, or data science) or a scientific discipline (e.g. biology, chemistry, materials science, biomedical science, or physics) with demonstrated expertise in computational research.
- Strong expertise in modern machine learning, statistical modelling and scientific computing, with experience developing and evaluating computational models and reproducible workflows following appropriate data management practices.
- Excellent programming skills, particularly in Python, and experience with relevant machine learning frameworks such as PyTorch, TensorFlow, JAX or scikit‑learn.
- Experience analysing complex scientific datasets in areas related to biomolecular design, materials discovery, chemical biology or formulation design.
What we offer
- 38 days annual leave
- Comprehensive range of childcare services
- Family leave schemes
- Cycle and electric car loan schemes
- Employee Assistance Programme
- Membership to a variety of social and sports clubs
- Discounted bus travel and season ticket travel loans
While this is a full‑time role, we welcome applications from individuals who wish to be considered for part‑time working or other flexible working arrangements.
How to apply
- A CV including publications, relevant technical projects, and links to representative code repositories, preprints or other technical outputs (e.g. GitHub, GitLab or personal website), highlighting your contributions.
- A 1‑page statement describing your previous work, relevant experience, and future research interests in AI for scientific discovery and/or biomedical research.
- The contact details of two referees.
The closing date for applications is 12 noon on 1 July 2026.
EEO Statement
Applications are particularly welcome from women, black and minority ethnic candidates, and those under‑represented in academic posts in Oxford. We, as a Department and Community, will be considerate and welcoming of all people, regardless of age, disability, gender reassignment, marriage and civil partnership, pregnancy and maternity, race, religion or belief, sex, sexual orientation, gender identity and socio‑economic background. Our policies, practices and Respectful Behaviours Framework underpin this commitment.
DPAG and Sustainability
We have signed up to The Laboratory Efficiency Assessment Framework (LEAF) and Green Impact, actively implementing eco‑friendly practices that reduce waste, promote energy efficiency and biodiversity.
Vacancy Information
Vacancy ID: 186868
