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Field
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considering a PhD or Clinical Doctorate, and many of our previous RAs have gone on to undertake these roles. The post holders will be based at Nottingham University and hold Honorary contracts with
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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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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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, 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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. Cranfield Doctoral Network Research students at Cranfield benefit from being part of a dynamic, focused and professional study environment and all become valued members of the Cranfield Doctoral Network
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University of Warwick and Schlumberger Cambridge Research Limited (SCR) Qualification: Doctor of Philosophy in Engineering (PhD) Eligibility: UK Students* Award value: Home fees and tax-free stipend
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at the point of commencing this post, with experience in contemporary molecular cell biology, tumour metabolism, experimental modelling and bioinformatics as applied to translational cancer genomics, ideally
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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