Coreshell Molecular Descriptor PDRA (845552)

You will develop high-throughput Python workflows and descriptors capturing assembly geometry, stability and organisation, working across simulation, machine learning and experiment. FTE: 1.0 FTE (full time) Contract Type: Fixed Term (12 months) The Department of Pure and Applied Chemistry is seeking an exceptional and highly motivated Research Associate to join the ARIA-funded CoreShell Fibre Foundry programme. The programme aims to develop a new approach to manufacturing hollow inorganic fibres using engineered proteins as reusable molecular fabrication units. This role is central to its computational design framework, translating molecular simulation trajectories into physically meaningful descriptors and practical design rules. You will develop high-throughput workflows for analysing coarse-grained molecular-dynamics simulations of candidate protein assemblies. This will include quantifying features such as curvature, lumen dimensions, scaffold and tube stability, packing order, aggregation state, interfacial organisation and assembly persistence. Working within an interdisciplinary team of computational chemists, machine-learning researchers, protein scientists and engineers, you will: develop high-throughput analysis workflows for coarse-grained molecular-dynamics simulations extract geometric, topological and structural descriptors from protein-assembly trajectories develop structure-property relationships linking protein sequence features to assembly behaviour generate reliable descriptor datasets for predictive and active learning models compare simulated behaviour with microscopy, scattering and other experimental data build robust, documented, reusable and version-controlled Python analysis pipelines contribute to project meetings, milestone reports, publications, presentations and research-data management. You will have a PhD in computational chemistry, molecular simulation, physics, biophysics, materials modelling, computational biology, mathematics or a closely related discipline. Strong Python skills, experience developing quantitative descriptors from molecular or structural data, and experience handling large trajectory datasets are essential. Knowledge of coarse-grained molecular dynamics, particularly Martini or a related force field, would be advantageous. Experience with GROMACS, VMD, MDAnalysis, OVITO, high-performance computing, structural-order analysis or the preparation of datasets for machine-learning models would also be beneficial. You will be able to conduct research independently while working effectively within a collaborative team. Strong attention to detail in computational analysis, software development and scientific data handling is essential. The post is full time and fixed term for 12 months. Initial interviews: date to be confirmed. Informal enquiries may be directed to Professor Tell Tuttle, Programme Lead, at tell.tuttle@strath.ac.uk please click here for full details.pdf