PhD studentship in Machine Learning for Computational Physics and Chemistry

PhD studentship in Machine Learning for Computational Physics and Chemistry, University College London, UK   

A 3.5-year PhD studentship is available to work under the supervision of Prof Jochen Blumberger at the Condensed Matter and Materials Physics Laboratory, University College London, UK. Interested candidates may want to work on one of the following two projects. 

Project 1: The overall aim of this project is to contribute to a step change in our fundamental understanding of electronically excited processes in molecular materials that are of direct relevance to energy conversion technologies. The project will contribute to this endeavour by developing machine learning methods for ultrafast calculation of electronic Hamiltonian matrix elements and their implementation in our in-house non-adiabatic molecular dynamics software X-SH. Your implementation will then be used to simulate the dissociation of excitons to charge carrier in organic solar cell materials or the conversion of a temperature gradient to electricity in thermoelectrics or to control motion of charge carriers in organics by electric fields. The applications will be carried out in collaboration with world-leading experimental groups. Interested candidates may want to take a look at our recent work, https://www.nature.com/articles/s41467-022-30308-5 and https://www.science.org/doi/10.1126/sciadv.adr1758

Project 2: The overall aim of this project is to develop and apply machine learning methods that enable a major boost of the time and length scales accessible to molecular dynamics simulations at ab-initio accuracy. Specifically, you will further develop our recently introduced perturbed neural network potential (PNNP) approach to leverage machine learning MD simulation of condensed phase systems in their electronic ground state interacting with external electric fields. Using this methodology you will investigate how electric fields modify chemical reactivity and ion adsorption at solid/liquid interfaces at atomistic resolution. Interested candidates may want to take a look at our recent work on perturbed neural network potential simulations:  https://www.nature.com/articles/s41467-024-52491-3    

Highly motivated students from Physics, Chemistry or Materials Science Departments are strongly encouraged to apply for this post. The candidate should have, or be about to receive, an honours degree (at least II.1 or equivalent) in Physics, Chemistry or a related subject.

Good knowledge in quantum mechanics and statistical mechanics is expected. Some experience with molecular simulation and scripting languages (e.g. python) is a plus.  

The start date of the studentship is 22. September 2025. The studentship covers university fees and maintenance at the standard UK rate. Due to funding restrictions, this studentship is open only to candidates from the UK or from the EU with pre-settled status in the UK.

Please submit applications in the following format:

• A personal statement (500 words maximum) outlining (i) your research experience, (ii) which project you would like to work on and your suitability for the project, (iii) what you hope to achieve in your PhD.
• A CV including full details of all University course grades, details on any scholarships, prizes and scientific papers published or in preparation.
• Academic transcripts for undergraduate (Bachelor) and graduate (Master) studies.
• Names, and email addresses of two academic or professional referees (at least one academic).

These four documents should be submitted as a single zip file to Jochen Blumberger, j.blumberger@ucl.ac.uk specifying in the subject line “PhD application”. The closing date for applications is 28. February 2025. Applications received after this date may be considered only if a suitable candidate has not been found by the above closing date.  

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