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Field
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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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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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only for up to 4 years full-time or up to a maximum of 6 years if studying on a part-time (0.5 FTE) basis How to apply: Send a copy of your CV and a 300-word statement about why you are interested in
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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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, usability, and insight into leakage dynamics across diverse constructions. Research Objectives The project is structured around three synergistic work packages: Descriptive Analytics: You will conduct a
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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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—specifically leveraging descriptive, predictive, and generative modelling techniques—to enhance test accuracy, usability, and insight into leakage dynamics across diverse constructions. Research Objectives
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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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nonlinear effects. These nonlinear effects will be generalised via correction terms discovered by machine learning from a large numerical simulated dataset. This dataset also allows for extending the theory
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working in a highly collaborative, dynamic environment. You’ll benefit from working alongside top academics and fellow researchers with a shared passion for innovation. About John Crane Ltd