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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
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strategies for robust statistical models. The project’s scope will be tailored to the candidate’s expertise, offering opportunities for innovation and impact. The successful applicant will join a dynamic
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experts in the prognostics and condition monitoring field, as well as being part of our strong and dynamic research centre at Cranfield University. About the host University/Centre Cranfield is an
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integrates dynamic “smart” materials into 3D-printed structures, opens new frontiers in both bioelectronics and solar energy harvesting. Our goal is to create adaptive electrode architectures. These advanced