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Risk-Relevant Biomarkers of Release Events) and one each for risk assessment (PhD-3 Quantitative Risk Assessment of Biofilm-Derived Hazards) and management (PhD-4 Predictive Modelling and Mitigation
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recognition Individual and group behaviour understanding Long-term behaviour prediction Multi-task learning and foundation models Zero-shot and open-set recognition Privacy-preserving AI using anonymised human
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of their remnants (including predictions for GW sources); mixing and transport processes in the stellar interior; nucleosynthesis and the origin of elements, including galacto-chemical evolution - which elements
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characterisation, to generate data-rich descriptions of evolving materials and processing pathways. A central aim is to couple these experiments with machine learning, mechanistic modelling, and automated data
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that are found readily available in the food supply. In animal models, emulsifiers cause inflammation in the gut, similar to that seen in Crohn’s disease. This project aims at investigating if removing emulsifiers
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models that can forecast the likely outcomes of current practices. The project aims to develop cutting-edge machine learning and statistical risk prediction techniques to predict each short-term, long-term