Research Associate in Machine Learning for Protein Design

Job number NAT
02255 Faculties Faculty of Natural Sciences Departments Department of Life Sciences Salary or Salary range £50,733 - £59,484 per annum Location/campus South Kensington and White City Campus
- On site only Contract type work pattern Full time
- Fixed term Posting End Date 3 Sept 2026

Please note interviews will be held on a rolling basis while the advert is live. It is therefore strongly recommended that you submit your application as early as possible. The advert will be closed once a suitable candidate is selected.

Are you interested in developing machine-learning methods for protein design and applying them to a new class of materials-manufacturing problem? The Research Associate in Machine Learning for Protein Design will work on Protein CAD, the computational protein-design tool of the CADMUS project , led by Dr James W. Murray at Imperial College London.

The project has a bold goal: to develop a new way of manufacturing advanced magnetic materials by using designed proteins as programmable scaffolds to organise inorganic particles with nanoscale precision. CADMUS brings together machine learning for protein design, engineering biology, materials science, magnetic characterisation and scalable biomanufacturing to test whether proteins can be used to control the structure of rare-earth-free magnetic materials in ways that are difficult to achieve with conventional manufacturing.

The programme is built around close collaboration between computational and experimental teams, with rapid design-build-test-learn cycles linking protein design, protein production, materials assembly and performance testing. Researchers joining CADMUS will be part of a highly interdisciplinary team working at the interface of AI, biology and materials science, with the opportunity to help create a new platform for sustainable functional materials manufacturing.

This job is part of an Advanced Research + Invention Agency-funded project, subject to contract negotiations.

You will develop and apply machine-learning and structural-bioinformatics methods for protein design, with a particular focus on modern generative approaches such as protein diffusion models, flow-based models and related methods. Your work will contribute to Protein CAD, a computational design framework for creating protein structures with specified shapes, interfaces and assembly properties.

You will build and maintain Python-based workflows for generating, evaluating and iterating protein designs. This will include implementing design constraints, analysing generated protein structures, benchmarking outputs, and incorporating feedback from experimental collaborators into successive design cycles.

You will work as part of the CADMUS consortium, collaborating with researchers at Imperial College London, the University of Sheffield and Change Bio. The project brings together protein design, engineering biology, materials science, magnetic characterisation and scalable biomanufacturing to explore whether designed proteins can act as programmable scaffolds for organising inorganic materials.

You will contribute to the scientific direction of the Protein CAD workstream, plan and prioritise your own research activities, maintain clear and reproducible computational records, prepare reports for the funder, present findings to collaborators and at conferences, and contribute to the high impact design of novel protein-templated materials.

We are looking for a motivated and collaborative researcher with a strong background in machine learning, structural bioinformatics or computational protein design. In particular, you will have:

• A PhD, or near completion of a PhD, in machine learning, structural bioinformatics, computational biology, computer science or a closely related discipline.
• Experience applying machine learning to protein structure, protein design or a related molecular problem.
• Strong Python skills and experience with a modern deep-learning framework.
• Knowledge of protein structure, protein-design principles and structural bioinformatics.
• An interest in generative protein design, ideally…