PhD Studentships: Machine learning for sustainable chemistry

Applications are invited for two PhD Studentships, starting October 2027, in the School of Chemistry at the University of Nottingham. The projects will focus on the development of new interpretable and interactive machine learning models and data-driven strategies for predicting consensus green chemistry metrics, enabling researchers to make AI-augmented rational assessments of different chemical synthetic routes.

The research will be supervised primarily by Jonathan Hirst and is part of a major award from the Royal Academy of Engineering Chairs in Emerging Technologies scheme, in which an open-source electronic lab notebook has been developed, deployed on the cloud at https://ai4green.app. The projects will involve close collaboration with synthetic chemists with a specific focus on green and sustainable chemistry. The studentships will provide a range of experience in computer programming and the development and application of machine learning algorithms to chemistry.

One of the studentships will have a significant business development component in the vein of an 'evangelist developer', i.e., someone who is technically skilled but also interested in promoting uptake of the software. This project is planned to be run in collaboration with and co-supervision from the Nottingham University Business School, with one of the strands of research focusing on understanding emerging AI-based technology business models, knowledge exchange processes and routes to market.

Funding: The studentships are fully funded for 42 months. Stipend at the RCUK rate (currently £21,805 per annum) and tuition fees will be paid at the UK rate. International students will have to pay the difference between UK and international fees.

Review of applications will start on Monday 7th December 2026, and the positions will be filled as soon as suitable persons have been found; hence applicants are encouraged to apply as soon as possible.