PhD Studentship: Machine Learning Density Functionals from Quantum Computing

Science

UK Other

Sunday 19 April 2026

SCI3057

Data is more valuable than oil, so it has been said. Quantum computing offers new unusual datasets thereby presenting new opportunities for AI approaches. Quantum computing is raising the prospect of calculations on a hardware architecture that matches the inherent nature of quantum chemistry electronic structure calculations and with it the opportunity to capture some of the inherent physics, albeit with the noise associated with near‑term quantum devices. This in turn offers an exciting new dataset from which it will be possible to use machine learning to train a more accurate functional for use in density functional theory. In collaboration with Phasecraft, a leading quantum algorithms company, this project will explore the generation of new quantum computing datasets and the development of machine learning techniques to utilize the datasets to train improved density functionals for use in quantum chemical electronic structure calculations.

Applicants should have, or expected to achieve, at least a 2:1 Honours degree (or equivalent if from other countries) in Chemistry, Physics, Mathematics, Computer Science or Natural Sciences 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 be essential. Studentships are open to home students only. The deadline to have completed and submitted your formal application is Sunday 19th April 2026. Start date is 1st October 2026.

Annual tax‑free stipend based on the UKRI rate (£21,805 for 2026/27) plus fully‑funded PhD tuition fees for the 3.5 years.

Jonathan Hirst (School of Chemistry), Katherine Inzani (School of Chemistry), Adam Gammon‑Smith (School of Physics & Astronomy)

For further details and to arrange an interview please contact Prof. Jonathan Hirst - (School of Chemistry)