PhD - Artificial Intelligence

School/Institution:University of Nottingham, United Kingdom, Nottingham 

Discipline: Artificial Intelligence

Employment Type:Full-time


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Job Description

Reference: SCI1921

Department: Chemistry

Applications are invited for a PhD Studentship,  in the School of Chemistry at the University of Nottingham. This project will bring together  expertise in new synthetic chemistry methodology [1, 2] and expertise in the application of artificial intelligence approaches in chemistry [3] to develop new predictive tools for C-H functionalisation reactions that will allow synthesis chemists to predict reaction outcomes and selectivities thereby accelerating drug discovery.

The project, which is funded by GSK and the University of Nottingham as part of a multi-million pound EPSRC-supported project “Accelerated Discovery and Development of New Medicines: Prosperity Partnership for a Healthier Nation”, will be supervised by Jonathan Hirst and co-supervised by Ross Denton. In addition, there will be close interactions with industrial co-supervisors at GSK. It will provide a range of experience in computer programming and the development and application of machine learning algorithms to chemistry.

Funding notes: The studentship is fully-funded for 48 months. Stipend at the RCUK rate (currently £15,009 per annum) and tuition fees will be paid at the UK/EU rate. International students must pay the difference between UK/EU and international fees.

Entry requirements: Applicants should have, or expected to achieve, at least a 2:1 Honours degree (or equivalent if from other countries) in Chemistry or a related subject. A MChem/MSc-4-year integrated Masters, a BSc + MSc or a BSc with substantial research experience will be highly advantageous. Experience in computer programming will also be beneficial.

If English is not the candidate’s first language, they must provide evidence before the beginning of the studentship that they meet the University minimum English Language requirements (IELTS 6.0 with at least 5.5 in each element).

To apply, students should initially contact Professor Hirst, tel: 0115 951 3478 or Email: , after which a formal application can be made via the University web site at:


  • Andrews, KG, Faizova R & Denton RM. A practical and catalyst-free trifluoroethylation reaction of amines using trifluoroacetic acid. Nat Commun, 2017, 8, 15913.
  • Beddoe RH, Andrews KG, Magné V, Cuthbertson JD, Saska J, Shannon-Little AL, Shanahan SE, Sneddon HF, Denton RM. Redox-neutral organocatalytic Mitsunobu reactions. Science, 2019, 365, 910.
  • Oglic D,Oatley SA; Macdonald SJF; McInally T; Garnett R; Hirst JD; Gärtner T. Active search for computer-aided drug design. Mol Inf, 2018, 37, 1700130

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