Postdoctoral Research Associate – Computational Polymer & Materials Discovery

Postdoctoral Research Associate – Computational Polymer & Materials Discovery

King’s College London
London, United Kingdom (Guy’s Campus)

King’s College London invites applications for a Postdoctoral Research Associate to join a cutting-edge, interdisciplinary research project funded under a EU Pathfinder initiative
. The successful candidate will contribute to the development of computational and machine-learning-driven frameworks for the discovery and design of novel sustainable polymers, with particular focus on Polyhydroxyalkanoate (PHA) materials aimed at sustainable food packaging solutions. The role combines molecular dynamics simulations, polymer modelling, and machine learning techniques
, and operates within the vibrant research environment of the Net Zero Centre and the King’s Institute for AI
. The postholder will work collaboratively with experimental partners in industry and academia and will have access to high-performance computing-based resources for large-scale simulations and data-driven approaches.

This is a full-time, fixed-term appointment (approximately 2.5 years
) based at King’s College London’s Guy’s Campus
. Researchers at King’s benefit from a supportive academic culture, flexible working arrangements, and inclusion in a diverse international community.

PhD in Chemistry, Physics, Materials Science, Chemical Engineering
, or a closely related discipline (completed or near completion).

Demonstrated ability to publish research outcomes in peer-reviewed scientific journals.

Proven research experience with atomistic molecular dynamics simulations of polymers and/or proteins for structure–property analysis.

Strong Python programming skills tailored to molecular simulation and cheminformatics tasks.

Familiarity with Unix/Linux environments and high-performance computing systems and version control tools (e.g., Git/Git Hub).

Excellent scientific communication skills, both written and oral, suitable for interdisciplinary collaboration.

Experience with
machine-learning interatomic potentials or machine-learning strategies integrated into molecular modelling.

Prior involvement in multicultural research collaborations and practice with advanced prediction methods for material properties.

Exposure to materials degradation modelling or related computational materials science workflows.

Competitive research salary commensurate with UK academic postdoctoral scales and inclusive of London weighting allowance.