PhD in Natural Sciences

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./nStart date is 1st October 2026./nSupervisors: Jonathan Hirst (School of Chemistry), Katherine Inzani (School of Chemistry), Adam Gammon-Smith (School of Physics & Astronomy).