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 ProteinCAD, the computational protein-design tool of the CADMUS project , led by Dr James W. Murray at Imperial College London.
CADMUS (Computer Aided Design of Materials for Universal Synthesis) is an ambitious UK research consortium from Imperial College London, the University of Sheffield and Change Bio, funded through the UK’s Advanced Research + Invention Agency’s (ARIA) Universal Fabricators programme (https://aria.org.uk/opportunity-spaces/manufacturing-abundance/universal-fabricators).
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 ProteinCAD, 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 ProteinCAD 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 with experience of diffusion, flow-matching or related models.
- The ability to deliver rigorous, reproducible research in a collaborative, milestone-driven environment and communicate findings clearly across disciplines.
- The opportunity to undertake high-risk, high-reward research and help build ProteinCAD within an ARIA-funded programme.
- Close collaboration with computational and experimental researchers across protein design, engineering biology and materials science.
- Access to Imperial’s computational environment, including HPC clusters, cloud computing and agentic GitHub Copilot tools.
- Support to develop an independent research profile through publications, conferences, training and future research proposals.
- Imperial’s career-development support, employment benefits and inclusive research culture.
- Grow your career: gain access to Imperial’s sector-leading dedicated career support for researchers as well as opportunities for promotion and progression.
- Be part of a diverse, inclusive and collaborative work culture with various staff networks and resources to support your personal and professional wellbeing.
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.
This job is part of an Advanced Research + Invention Agency-funded project, subject to contract negotiations.
The post will be based in the Department of Life Sciences on the South Kensington campus, with collaborators at Change Bio (London), and University of Sheffield.
We welcome applications from candidates who do not meet every desirable criterion but have relevant expertise and a strong interest in developing new skills.
The post is full time and fixed term for 18 months, with the possibility of extension.
Expected start date: as soon as possible.
If you require any further details about the role, please contact: Dr. James Murray – [email protected]
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