Research Associate/ Fellow (Fixed Term)

About The Role

We are seeking a highly motivated Research Associate/Fellow to join the Hydrogen Research Group at the University of Nottingham. The successful candidate will become part of a multidisciplinary team of scientists and engineers working at the forefront of hydrogen materials research. The project will focus on the optimisation of metal (complex) hydrides and the development of advanced solid-state hydrogen storage and compression technologies. A key aspect of the role will be the computational discovery, design, and modelling of metal (complex) hydrides using state-of-the-art atomistic and data-driven approaches.

About The Team

The Hydrogen Research Group comprises five academic staff members, eight research fellows, and eight PhD researchers. The group brings together internationally recognised expertise in both experimental and computational hydrogen materials research, spanning materials synthesis, characterisation, atomistic modelling, and materials informatics. We maintain strong collaborations with industrial partners and leading research institutions worldwide, enabling impactful and translational research in hydrogen technologies. We are committed to providing a supportive, collaborative, and inclusive research environment and welcome applications from individuals with diverse backgrounds, experiences, and career pathways.

About You

• You will hold, or be close to completing, a PhD (or equivalent qualification) in a relevant discipline such as Chemistry, Physics, Materials Science, or a related field.
• You should have strong expertise in the atomistic computational modelling of solid-state materials, with experience in density functional theory (DFT) and/or machine learning interatomic potentials.
• We Welcome Applicants With a Broad Range Of Research Interests And Experiences Who Can Contribute To Our Multidisciplinary Research Programme.
• Candidates Should Demonstrate Experience In At Least One Of The Following Areas:
  • Developing, implementing, or adapting Python-based codes and workflows for training machine learning interatomic potentials for solid-state materials.
  • High-throughput computational screening and materials discovery, including the application of machine learning methods.
  • CALPHAD modelling and thermodynamic assessments.
  • Phase-field modelling of materials behaviour and evolution.

What We Offer

• A friendly, diverse, and supportive working environment.
• Generous holiday entitlement (plus bank holidays and university closure days).
• Access to training, development, and career progression opportunities.
• Staff discounts, travel schemes, and a wide range of additional benefits.

Further Information

This is a Fixed-Term position available for 2 years. Working hours are 36.25 hours per week (full-time).