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of tomorrow and creating novel solutions to major global challenges. Our community is made up of 13 000 students, 400 professors and close to 4 500 other faculty and staff working on our dynamic campus in Espoo
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clinicians but not yet ‘at threshold’ for secondary care. Despite this, the UK has no settled service structure to address this gap, with the consequence that alternate diagnoses are missed and genuine
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, embedded intelligence, and adaptive cyber-physical systems that operate safely under uncertainty and dynamic conditions. This PhD at Cranfield University explores the development of resilient, AI-enabled
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freshwater fishes, structured around the following objectives: Use the LOC to map the freshwater fish distributions in Madagascar, including threatened, invasive and human food species Create predictive models
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Embark on a ground-breaking PhD project harnessing the power of Myopic Mean Field Games (MFG) and Multi-Agent Reinforced Learning (MARL) to delve into the dynamic world of evolving cyber-physical
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overcomes the geographic limitations of conventional systems, enabling global scalability and accessibility. Using advanced computational fluid dynamics (CFD) approaches, the project is aimed at advancing
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explore or optimise the flexible structures and manufacturing process of Litz wires. This studentship offers the opportunity for the PhD student to lead the development of innovative simulation tools
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, lack of transparency, safety assurance, and sustainability. You will work at the forefront of AI research, exploring formal and dynamic verification methods, explainable AI, and data space integration
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load emulation, surface tribology and lubricants, contact mechanics or dynamical phenomena. This is an opportunity to work within a world-class multidisciplinary team within the Engineering Systems
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dynamic environments, including narrow spaces and interactions with unfamiliar objects. This project aligns with Rolls-Royce’s technical needs for developing soft robotic solutions to enable in-situ/on-wing