About the project
A fully funded 3.5-year PhD studentship is available in the area of computational materials discovery, as part of a prestigious international Synergy grant funded by the European Research Council. The project, ‘Autonomous Discovery of Advanced Materials’, aims to revolutionise the way that new materials are discovered by combining computational simulations, robotics and materials synthesis.
Within this studentship, you will develop methods for analysing the results of crystal structure prediction studies, applying unsupervised machine learning to investigate structural families that occur across the landscapes of related molecules and applying supervised methods for predicting properties of predicted crystal structures. Computational methods are developing rapidly in this area and you will join an internationally-leading group in the development and use of crystal structure prediction (CSP) methods. The student will develop methods and also be involved in collaborations with lab-based materials discovery, where the computational results are used to guide robots to perform large-scale experiments.
The project is based in the computational materials discovery research group led by Prof. Graeme Day in the School of Chemistry at the University of Southampton, who have pioneered the use of CSP for the discovery of functional molecular materials. You will be part of a multi-disciplinary team which includes collaborators at the University of Liverpool and Rostock University. Through these collaborations, you will interact with other computational chemists, synthetic chemists and engineers developing the use of robots in the materials chemistry laboratory.
Applicants should hold, or expect to obtain, a good degree (equivalent to a UK first or upper second class) in chemistry, materials science or a related discipline. We are looking for candidates with an enthusiasm for research, multidisciplinary collaboration and tackling challenging problems through teamwork. You do not need to have experience with crystal structure prediction methods. Experience with computational chemistry and programming would be an advantage, as well as excellent communication skills.
