PhD Studentship in Machine Learning Molecular Dynamics

About the Project

 The project involves the development and application of machine learning methods that enable a major boost of the time and length scales accessible to ab-initio/first principles molecular dynamics simulations. You will contribute to the development of novel work flows as well as to the training, testing and application of latest neural network methodologies. Applications will focus on the atomistic simulation of mineral oxide/liquid water interfaces that are of relevance to solar fuel production, decontamination of soil and geochemical transformations. The simulations will uncover the interfacial solvation structure, validated through comparison between computed and experimental sum-frequency vibrational spectroscopies, as well as the mechanism of chemical transformations at such interfaces, and how they are influenced by external electric fields and electrolyte composition. Access to high performance computing facilities including GPU clusters will be provided. Interested candidates may want to take a look at our recent work on machine learning molecular dynamics:  https://www.nature.com/articles/s41467-024-52491-3

Applications from students from the UK, EU and overseas are welcome. The studentship will cover university fees at the UK (home) rate and includes funds for maintenance at the standard rate and for participation in international conferences and workshops. Shortlisted applicants from the EU who do not have pre-settled status in the UK and shortlisted applicants from overseas countries have the opportunity to apply for an International Scholars Award for Doctoral Training that waives the difference between home and oversees fees. 

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 (Project 1 or 2) 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 from candidates from the UK and from EU with pre-settled status in the UK is 26 January 2026. The closing date for applications from candidates from the EU who do not have pre-settled status and from Overseas is 9 January 2026. 

The PhD student will become a member of the UCL Doctoral Training Centre (CDT) for Data Intensive Science, which administers the PhD studentship. Please submit, in addition to the above, an application to the DTC by the above deadline following the instructions given here:  

https://www.ucl.ac.uk/data-intensive-science-industry/study-here/how-apply

Informal enquiries regarding both positions can be made to Jochen Blumberger by email, j.blumberger@ucl.ac.uk. 

The start date of the studentships is 1 October 2026.

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