The successful candidate will develop and operate high-throughput computational screening workflows for MOFs, integrating molecular simulation, automated structure generation and large-scale materials databases. The post holder will lead computational research on the design, screening and optimisation of MOFs for target adsorption applications. In addition, the successful candidate will perform advanced molecular simulations of adsorption, including quantum calculations, grand-canonical Monte Carlo (GCMC) and molecular dynamics (MD), to characterise and predict the performance of candidate materials.
Salary is £47,186-£ 50,942 per annum inclusive of London allowance. This is a fixed-term role until 31st of December 2029 in the first instance.
The successful candidate will hold (or shortly expect to obtain) a Ph.D. in Machine Learning, Physics, Chemistry, Chemical Engineering, Materials Science or a related quantitative discipline (or equivalent industry experience), together with an undergraduate degree (2(i) minimum) or M.Sc. in a relevant field. The post holder will have demonstrable experience with atomistic/molecular simulation methods, alongside knowledge of nanoporous materials (e.g. MOFs, zeolites or porous polymers) and adsorption processes for gas capture, with experience or knowledge of material informatics and the analysis of large materials datasets. Demonstrable experience or knowledge of computational modelling and data-driven approaches, including machine learning methods, optimisation techniques, and relevant programming skills is essential.
As well as the exciting opportunities this role presents we also offer some great benefits some of which are below:
Our department holds an Athena SWAN Gold award, in recognition of our long-term commitment and 'beacon' status in advancing gender equality.
Tagged as: Life Sciences
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