Postdoctoral Research Scientist - AI for Bionanoscience in Oxford

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 or both of these areas depending on expertise and interests. The role is highly multidisciplinary and collaborative, involving close collaboration with experimental and computational researchers, with opportunities to develop AI systems capable of advancing scientific discovery in areas including advanced therapeutics and disease diagnostics. Postholders will work within a vibrant research environment comprising multiple interconnected projects in fundamental and applied bionanoscience, and will collaborate with internationally recognised academic and industrial partners. The Stevens Group is internationally recognised for pioneering work in biomaterials research and operates at the interface of materials science, chemistry, biology, bioengineering and medicine, enabling highly collaborative and multidisciplinary research. The lab is strategically affiliated with the Department of Physiology, Anatomy and Genetics and the Institute of Biomedical Engineering within the Department of Engineering Science.

We welcome applicants from machine learning, AI, computer science, computational chemistry, physics, mathematics and related quantitative disciplines. Candidates with strong computational and methodological expertise who are excited to apply AI to challenging scientific problems are encouraged to apply, even if they have limited prior experience in biology, chemistry or materials science.

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.
• Work closely with experimental and computational researchers to define tractable machine learning problems, analyse complex multimodal scientific 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.

Qualifications

• Hold, or be close to completing, a PhD/DPhil in either 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, including experience developing and evaluating computational models and reproducible workflows following appropriate data management practices.
• Strong 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 particular related to biomolecular design, materials discovery, chemical biology, formulation design, or related areas.

Institute & Department Overview

The 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. In 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.

Benefits

• An excellent contributory pension scheme
• 38 days annual leave
• A 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