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 are also expected to have the opportunity to engage with events, training and personal development activities run by the Royal Society.
The posts are ideal for individuals interested in working creatively across a range of projects and experts, working closely with academics in Chemistry and Imperial's School for Human and Artificial Intelligence. The broader research environment also includes the EPSRC-funded AI hub for Chemistry (AIchemy), Co-Directed by Prof. Jelfs.
The three posts are:
- Research Associate in Computational Chemistry. You will focus on the development of computational screening methods and property prediction models using both data-driven and simulation approaches relevant to a range of organic materials. This will also include the development of approaches to determine factors such as solubility, and thermodynamic and kinetic stability of promising materials.
- Research Associate in AI for Chemistry. You will develop methods for data-extraction from the literature, generative AI, as well as explore the training of LLMs to assist in guiding the synthesis of promising materials, using both software tools you develop and data collected by the larger team. You will also explore the integration of human-in-the-loop feedback to improve the models.
- Research Associate in Neurosymbolic Learning for Chemistry. 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 research experience, there is the possibility to be appointed at a higher spine point, where in addition to research, you 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. Candidates interested in being considered for this level of position should indicate this within their application. Candidates in their early career post-PhD are expected to start at the lower end of the salary range.
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:
A start date from 1st January 2026 or thereafter is available.
This is a full-time post (35 hours per week) and fixed term 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
Application deadline: 11th November 2025
For more information and to apply visit https://www.jobs.ac.uk/job/DPB378/research-associate-positions-in-computational-chemistry-ai-in-chemistry-and-in-neurosymbolic-learning-for-chemistry
