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, alongside our Doctoral Researchers Core Development programme (transferable skills training), provide those studying a research degree with a wealth of social and networking opportunities. How to apply
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-informed data analytics tools for the predictive maintenance (PdM) strategy applications to high-value critical assets. Among others, the recently developed Physics-informed Neural Network (PINN) technique
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are part of the programme. The research is funded by the Centre of Propulsion and Thermal Engineering at Cranfield University. The work will be conducted at the Cranfield icing wind tunnel (IWT) based
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challenge in the UK's Net Zero transition. Current satellite dependent navigation remains vulnerable to interference, jamming and signal degradation, causing serious problems for safe and efficient transport
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” highly flexible components and ensure their energy harvesting capabilities. This project will contribute to ambitious plans for SBSP as a vital part of the future Net Zero landscape. Recent advances in
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supporting the Net Zero 2050 target. This PhD project will develop an AI-enabled framework that optimizes wind turbine control and predictive maintenance. Using Deep Reinforcement Learning (DRL), the system
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members of the Business Disability Forum and Stonewall University Champions Programme. Cranfield Doctoral Network Research students at Cranfield benefit from being part of a dynamic, focused and
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access to state-of-the-art facilities and a network of professionals in the field. The expected impact of this research will be the development of valuable insights into how advanced technologies can be
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Advances in computing, experiments, and information will continue to reshape engineering in the next decade. This PhD position will nurture a multidisciplinary innovator with the tools to unravel
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requirements A minimum of a 2:1 first degree in a relevant discipline/subject area (e.g. aerospace, automotive, mechanical, electrical, chemical, computing, and manufacturing) with a minimum 60% mark in