Research Associate in Informed Machine Learning for Chemistry

Location: White City Campus

About the role:

Funded by a Royal Society Faraday Discovery Fellowship, you would be working on the project "Predicting synthesisable materials: bridging the gap between computation and experiment", working at the interface of Chemistry and Artificial Intelligence (AI). This is one of just seven long-term £8M projects funded in the UK (royalsociety.org/news/2025/08/faraday-discovery-fellowship).

This is an exciting opportunity to design and implement novel digital technologies in collaboration with a wide range of academic collaborators. You will be part of a larger team of Research Associates, PhD students, and a technician consisting of experimental chemists, computational chemists, and computer scientists specialising in AI.

You will focus on the development of methods for predicting the synthesisability of organic molecules, including consideration of retrosynthesis, alongside exploring integration of logic requirements for factors such as toxicity, safety and sustainability. You will also explore the integration of human-in-the-loop feedback to improve the models.

For those with significant experience post-PhD, there is the possibility to be appointed at a higher spine point, where in addition to research, the post holder would be expected to assist in the day-to-day running and strategic direction of Prof. Jelfs' research group, including supervision of postgraduate and undergraduate project students, co-managing and developing collaborations, kick-starting new research programmes, submission of publications and grant applications and recruitment. This is an excellent opportunity for a candidate looking to gain the experience needed to pursue a long-term career in academia.

Duties and responsibilities:

• Develop and apply novel graph learning based methods for predicting the synthesisability of organic molecules
• Develop and apply informed machine learning methods to chemical problems

If appointed at a higher spine point: 

• To assist in the day-to-day running of the Jelfs research group including, but not restricted to, supervision of postgraduate and undergraduate project students and PDRAs, co-managing and developing collaborations (academic and industrial), kick-starting new research programmes and oversight of the strategic development of the group, writing and submission of publications and grant applications. 

Essential requirements: 

• Experience in developing novel models for learning on graphs
• Experience in generative models and developing novel methods for them
• Knowledge of informed machine learning
• Experience of dealing with multidisciplinary experimental and theory collaborators
• Practical experience within a research environment and publications in relevant journals
• (For appointment at a higher spine point): experience as a post-doctoral research associate, experience of grant writing, experience of managing research programmes, supervising junior researchers, ability to build productive working relationships. 

What we can offer you: 

• The opportunity to continue your career at a world-leading institution and be part of our mission to continue science for humanity.
• Grow your career: gain access to Imperial’s sector-leading dedicated career support for researchers as well as opportunities for promotion and progression.
• Sector-leading salary and remuneration package (including 41 days off a year and generous pension schemes).
• Be part of a diverse, inclusive and collaborative work culture with various staff networks and resources to support your personal and professional wellbeing. 

Further Information: 

This is a full-time post (35 hours per week). This role is for a fixed-term contract for 36 months. 

Candidates who have not yet been officially awarded their PhD will be appointed as a Research Assistant. 

If you require any further details about the role, please contact: Prof. Kim Jelfs, k.jelfs@imperial.ac.uk                                                 

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