Research Fellow in Computational Chemistry and AI/ML for Sustainable Polymers

Location: Edgbaston, Birmingham UK

Full time starting salary is normally in the range £36,636 to £46,049 with potential progression once in post to £48,822

Fixed Term contract up to October 2030

Travel may be required for this role

Description of the Group

We are seeking a highly motivated Research Fellow to join the EPSRC-funded Prosperity Partnership project “Mission Biodegradability: Foundations for the Sustainable Future of Formulated Polymers.” The successful candidate will contribute to cutting‑edge research at the interface of computational chemistry, polymer modelling, and machine‑learning approaches to predicting polymer biodegradation, working under the supervision of Prof. David Scanlon.

In this role, you will conduct original research, generate high‑quality publications, and present your findings through written reports, group meetings, and wider project collaborations. You will play an active part in the Scanlon Materials Theory Group, contributing to project management, group activities, and—where appropriate—the supervision and training of research students.

The position involves close collaboration with project partners, requiring engagement in joint meetings and interdisciplinary teamwork.

This post will be hosted in the group of Professor David O. Scanlon in the School of Chemistry. The Research Fellow will contribute computational insight to support the development of predictive frameworks for sustainable polymer design. You will have appropriate research experience to undertake this project, alongside a proven ability to conduct and publish research in polymer science/computational chemistry.

Person Specification

You'll have a PhD and established research profile in computational chemistry, polymer modelling, and machine-learning/AI.

You should be able to demonstrate: experience with machine learning methods applied to scientific problems, preferably with materials or polymer datasets; proficiency in Python and scientific computing libraries; familiarity with machine-learning frameworks and data processing tools; experience with molecular modelling or simulation tools (e.g., LAMMPS, GROMACS, or similar) and the ability to design and analyse simulation workflows.

Informal enquiries can be made to Professor David Scanlon, d.o.scanlon@bham.ac.uk

To download the full job description and details of this position and submit an electronic application online please click on the 'Apply' button above.

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We value diversity and inclusion at the University of Birmingham and welcome applications from all sections of the community and are open to discussions around all forms of flexible working

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