Postdoctoral Research Associate in Machine Learning for Chemistry

Postdoctoral Research Associate in Machine Learning for Chemistry Inorganic Chemistry Laboratory, South Parks Road, OX1 3QR Applications are invited for a Postdoctoral Research Associate in Machine Learning for Chemistry to work in the research group of Professor Volker Deringer at the Department of Chemistry.

About the role

The post is based in the Inorganic Chemistry Laboratory, and is funded by a prestigious UKRI Frontier Research grant. The research aims to advance the atomistic understanding of amorphous materials, with a particular focus on predicting nuclear magnetic resonance (NMR) parameters and using these predictions to inform structural models. The successful candidate will parameterise and validate machine-learning models based on first-principles data and apply these methods across increasingly complex structures and chemical systems.

The role will involve both methodological development (datasets, models, and contributions to open-source software) and application of these methods to research problems in computational materials chemistry. You will also contribute to wider-ranging research activities within the group, including internal and external collaboration, knowledge sharing, and supporting junior researchers where appropriate.

This is a fixed-term post for up to 12 months, available as soon as possible. The role is offered on a full-time basis, although part-time hours, hybrid working, and flexible working arrangements will be considered.

Find out more about the research and group at:   ?url=https%3A%2F%2Fderinger.chem.ox.ac.uk&module=jobs&id=3226251" target="_blank" rel="nofollow">?url=https%3A%2F%2Fderinger.chem.ox.ac.uk&module=jobs&id=3226251" target="_blank" rel="nofollow">https://deringer.chem.ox.ac.uk

About you

You will hold a PhD in Chemistry or a closely related discipline (or be close to completion at the time of appointment) and have a strong background in computational chemistry and machine learning applied to materials research.

You will demonstrate:

• Experience in development, parametrisation, validation and application of atomistic machine-learning models (machine-learned interatomic potentials or models predicting atomic properties such as NMR parameters)
• Experience working with high-performance computing environments and managing research data and computational resources
• A strong publication record (e.g. peer-reviewed publications or preprints) demonstrating your contributions to previous research
• Excellent organisational, analytical, and communication skills

You will have the ability to work both independently and collaboratively within an interdisciplinary research environment. Experience in the atomistic modelling of amorphous materials or contributing to open-source scientific software would be an advantage.

Application process

Applications for this vacancy are to be made online. You will be required to upload a supporting statement and CV as part of your application. Your supporting statement (5 pages) should clearly and concisely explain how you meet each of the selection criteria found in the job description, with reference to your skills and experience. It is strongly recommended to provide this statement as an itemised list.

As part of the application, you will also be asked to provide details of two referees.

Certificates, references and research papers should not be provided at this stage.

The closing date for applications is 12.00 midday, Monday 13 April 2026, UK time. Interviews will be held as soon as possible thereafter.

The University of Oxford and The Department of Chemistry are Silver Athena SWAN holders. Applications are particularly welcome from women and black and ethnic minority candidates, who are under-represented in Chemistry research posts in Oxford.

Contact Person : HR Team Vacancy ID : 185758

Contact Phone : Closing Date & Time : 13-Apr-2026 12:00

Pay Scale : RESEARCH GRADE 7 Contact Email : hrchem.ox.ac.uk

Salary (£) : £39,424 - £47,779 per annum

Click on the link(s) below to view documents

Filesize

185758 Postdoctoral Research Associate Job Description 496.8