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trustworthy machine learning, with a particular emphasis on mechanistic interpretability and its application to healthcare data. The successful candidate will contribute to understanding how modern machine
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remains from archaeological and forensic contexts. These resources will contribute towards the Continuing Professional Development of relevant practitioners, as well as provide key learning resources
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team, which as of January 2026 comprises two PDRAs, three PhD students and two MChem students, with three different nationalities. We work on ligand design and synthesis, transition-metal catalysis
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ecology that challenges Western narratives and explores alternative ways of thinking and living with weeds by learning from traditional knowledges in their places of origin; a multispecies geography that