Research Fellow in Computational Materials Science - School of Chemistry Grade 7

Position Details

• School of Chemistry
• Location:
  
  University of Birmingham, Edgbaston, Birmingham, UK
• Full time starting salary: £36,636 to £46,049 (potential progression to £48,822)
• Grade: 7
• Full Time, Fixed Term contract up to May 2028
• Closing date: 8th February 2026
• UK and international travel may be required for this role

We are seeking a Postdoctoral Research Associate to join the Research Group of Dr Adam Michalchuk, funded by the US Air Force Office of Scientific Research. The position will be to develop and use computational models to explore the ultrafast processes that underpin the mechanochemical reactivity of energetic materials (EMs; explosives, propellants, pyrotechnics), and collect inelastic neutron scattering spectra to support model validation.

The project will be in close collaboration with world‑leading EM synthesis experts at Ludwig Maximilian University of Munich, Germany.

• Work within the Michalchuk group’s research grants and projects and contribute to writing bids.
• Work within and support computational materials modelling and inelastic neutron scattering spectroscopy activities of the group.
• Work with local, national, and international collaborators to successfully deliver high‑quality research outcomes.
• Analyse and interpret research findings and results.
• Contribute to disseminating research findings via appropriate platforms (such as conferences and academic publications).
• Contribute to generating funding.
• Contribute to licensing or spin‑out deals with demonstrated commercial success (such as revenues, asset or company sales, IP generated) and/or public understanding of the discipline or similar.

• Take a prominent role in delivering the research goals of the group.
• Conduct a research program, defined in agreement with the group.
• Contribute to writing bids for research funding (including facility access).
• Analyse and interpret data.
• Apply knowledge in a way which develops new intellectual understanding.
• Disseminate research findings for publication, research seminars etc.
• Supervise students on research related work and provide guidance to PhD students where appropriate to the discipline.
• Contribute to developing new models, techniques and methods.
• Undertake management/administration arising from research.
• Contribute to Departmental/School research‑related activities and research‑related administration.
• Contribute to enterprise, business development and/or public engagement activities of manifest benefit to the College and the University, often under supervision of a project leader.
• Collect research data; this may be through a variety of research methods, such as scientific experimentation, computer or numerical simulation, literature reviews, and research interviews.
• Present research outputs, including drafting academic publications or parts thereof, for example at seminars and as posters, and for regular project updates to funders and collaborators.
• Provide guidance, as required, to support staff and any students who may be assisting with the research.
• Deal with problems that may affect the achievement of research objectives and deadlines.
• Promote equality and values diversity acting as a role model and fostering an inclusive working culture.
• Ensure compliance with health and safety, and in data security and research integrity, in all aspects of work.
• Travel to national facility and research collaborator laboratories as necessary.

• The candidate should have, or be nearing completion of, a PhD (or equivalent) in computational materials chemistry/physics, or related relevant area.
• The candidate must have demonstrable experience working with high‑performance computing infrastructure.
• The candidate must have a strong track record in programming, preferably in Python.
• The candidate should have demonstrable expertise in computational modelling of solids, preferably using density functional theory methods.
• Previous experience in the use and/or development of machine‑learned interatomic potentials would be seen as a significant asset.
• Previous experience of inelastic neutron scattering spectroscopy (or similar) would be seen as favourable.

• H…