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learning to quantify uncertainty and forecast potential faults. c) Trustworthy Autonomous Recovery: Autonomous self-healing must be verifiable; this challenge is met by designing AI-driven decision engines
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fibroblast identity and function within liver tumours and how these cells shape anti-tumour immune responses. The student will use in vivo cancer models, spatial tissue analysis and immunological profiling
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Compliance Engine to embed regulatory rules into the digital twin environment. Project Timeline Year 1 (Month 1-12): WP1 and associated training to obtain core skills in digital twin modeling and adversarial
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their suitability for real-time emergency decision-making. This project addresses this challenge by combining physics-based modelling with data driven surrogate approaches. The first stage of the project will involve