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fundamental algorithms for producing policies for rich goal structures in MDPs (e.g. risk, temporal logic, or probabilistic objectives), and modelling robot decision problems using MDPs (e.g. human-robot
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Leedham (colorectal cancer biology), Dan Woodcock (cancer genomics), Helen Byrne (mathematical modelling), and Jens Rittscher (computational pathology and imaging AI), offering a unique opportunity to work
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, attack surfaces, defensive mechanisms and related topics to the safe deployment of systems contain multiple LLM and VLM powered models. You will be responsible for Developing and implementing; capability
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system planning, system modelling, multi-criteria decision analysis, data science, and machine learning. You will perform stakeholder engagement and on-the-ground data collection in LMIC contexts and
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, the centre will initially focus on some of the following thematic areas: • Decision analysis under model misspecification • Uncertainty quantification around LLMs • Constrained optimal
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sufficient specialist knowledge in energy systems or energy access, and its application to policy and/or economic analysis, and be proficient in Python programming, data analytics, energy systems modelling
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types; probabilistic model-checking; probabilistic verification/synthesis; probabilistic logics and semantics; planning and game-theoretic methods; or, where appropriate, software implementation in