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analytical backbone of the programme. It develops sensor-enabled diagnostic cells, multi-modal data pipelines and hybrid physics-informed machine learning approaches to understand interfacial behaviour during
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future of formulated polymers. We are seeking a Research Fellow in Computational Chemistry and AI/Machine Learning to advance the state of the art in understanding the degradation and biodegradation
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denotational semantics, abstract machines, as well as string diagrams and graph rewriting. Some knowledge of category theory would be useful but not essential. Being able to formalise the frameworks and
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such as geotechnical engineering, archaeology, geophysics, sensing and machine learning, with group members from seven different countries, speaking over ten different languages. Perks of this role include
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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