About the Project

A PhD studentship using machine learning to accelerate the discovery of energy materials is available in the research group of Dr Alex Ganose based in the Department of Chemistry, Imperial College London (https://virtualatoms.org). This studentship is available to UK students only.

Deadline: review of applications will start in June 2023, and the positions will be filled as soon as possible thereafter; hence you are encouraged to apply as soon as possible.

To apply, please send your CV and a cover letter to [email protected].

Project Aims

This project will develop new machine-learning approaches to predict the electronic properties of materials. These new methods will be used for the inverse design of new materials with tailored properties for use in renewable energy applications, including photovoltaics and thermoelectrics. Promising candidates will be validated using quantum mechanical simulations run on high-performance compute clusters and through experimental collaborations. The candidate will extend state-of-the-art machine learning methods to new high-dimensional electronic properties to improve the reliability of materials discovery efforts.

The project will be supervised by Dr Alex Ganose and will involve close collaboration with computational and experimental partners. The project will provide a range of experience in computational materials chemistry and machine learning as well as opportunities to develop programming, scientific communication, and team-working skills.

Funding Notes

This PhD studentship is fully funded for 42 months starting in October 2023. This funding covers the payment of tuition fees at the UK/home rate and gives you a tax-free stipend at the standard UKRI rate (currently £19,668 per year). Please contact Dr Alex Ganose ([email protected]) for further information.

More information is available here: https://www.findaphd.com/phds/project/machine-learning-to-accelerate-the-discovery-of-energy-materials/?p158060