University of Oxford  Jobs

University of Oxford

Applications Close:

Oxford, UK

5 Star Employer Ranking

"DPhil Studentship in Computational Chemistry: Machine Learning Interatomic Potentials for Metal-Ligand Interactions"

Academic Connect
Applications Close

DPhil Studentship in Computational Chemistry: Machine Learning Interatomic Potentials for Metal-Ligand Interactions

DPhil Studentship in Computational Chemistry: Machine Learning Interatomic Potentials for Metal-Ligand Interactions

University of Oxford

Qualification Type:PhD
Location:Oxford
Funding for:UK Students
Funding amount:£20,780
Hours:Full Time
Placed On:15th January 2026
Closes:30th January 2026
Reference:FD/Chem/2026

Supervisor: Professor Fernanda Duarte

Start date: 1st October 2026

Applications are invited for a fully-funded DPhil studentship in Machine Learning Interatomic Potentials for Metal-Ligand Interactions available from October 2026, to work under the supervision of Professor Fernanda Duarte in the Department of Chemistry at the University of Oxford.

The studentship will cover course fees at a Home rate and provide a stipend of no less than the standard UK Research Council rate (currently set at £20,780 p.a.) for 4 years. Please note the eligibility criteria set out by the UKRI at: https://www.ukri.org/what-we-do/developing-people-and-skills/esrc/funding-for-postgraduate-training-and-development/eligibility-for-studentship-funding/

The successful candidate will join the Duarte group in the Department of Chemistry to develop innovative strategies for generating Machine Learning Interatomic Potentials (MLIPs) that accurately capture the dynamic nature of metal-ligand interactions. These models will enable predictive simulations of structural, thermodynamic, and kinetic properties in complex systems relevant to catalysis, supramolecular chemistry, and biology. The candidate will benefit from close collaboration with experimentalists, training in a range of computational techniques, as well as being part of a supportive, diverse, and international research team. The Duarte group focuses on the development and application of computational approaches to model chemical processes across multiple size and time scales. The team has pioneered methodologies for exploring chemical reactivity, including tools such as autodE and mlp-train, which form the foundation of this project. More information can be found on the group website.

Candidates with a first-class or strong upper second-class undergraduate degree in Chemistry or a related subject are encouraged to apply. The candidate is expected to have a strong commitment to research and should have demonstrated the ability to independently learn new skills. The successful applicant will be based in the Physical and Theoretical Chemistry Laboratory (PTCL), Oxford.

Candidates should submit a formal application for DPhil in Chemistry via the Oxford online application system (via the above 'Apply' button):

https://www.ox.ac.uk/admissions/graduate/application-guide
https://www.ox.ac.uk/admissions/graduate/courses/dphil-chemistry

Please quote FD/Chem/2026 under ‘Departmental Studentship Applications’.
Application deadline: 12.00 noon UK time on 30 of January 2026.

Queries relating to the application and admission process should be directed to: graduate.admissions@chem.ox.ac.uk; tel.: +44 (0) 1865 272569.

The Department of Chemistry holds the Athena SWAN Silver Award and the Duarte group is dedicated to promoting diversity, equality and inclusion.

10

Unlock this job opportunity


View more options below

View full job details

See the complete job description, requirements, and application process

Stay on their radar

Join the talent pool for University of Oxford

Join Talent Pool

Express interest in this position

Let University of Oxford know you're interested in DPhil Studentship in Computational Chemistry: Machine Learning Interatomic Potentials for Metal-Ligand Interactions

Add this Job Post to FavoritesExpress Interest

Get similar job alerts

Receive notifications when similar positions become available

Share this opportunity

Send this job to colleagues or friends who might be interested

263 Jobs Found
View More