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records that can systematically inform preparedness, training and future response. As a result, learning from past events is fragmented, inconsistently captured, and insufficiently embedded into emergency
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models Create problem-solving skills and ability to work independently *Candidates with a PhD in other disciplines may be eligible if they can demonstrate exceptional problem-solving skills and deep
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explore how goal-driven AI agents can generate and execute robot programmes automatically while learning from and collaborating with human partners. The research will investigate hybrid approaches
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, analyse learning from past incidents and exercises, and co-develop practical frameworks or tools to support improved cross-agency working. These will be tested through scenario-based evaluation and
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data set (e.g. neutron irradiations, that take years/decades to generate). Digilab brings AI/ML (artificial intelligence / machine learning) approaches for data engineering and automation to utilise