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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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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
opportunities. As part of Cranfield University’s strong industry and research network, the student will have the chance to attend international conferences and present findings at key events in the fields
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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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strategies that can improve sustainability and resilience in decentralised manufacturing networks. A unique selling point of this project is the opportunity for collaboration with world-leading experts through
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at international conferences and build a professional network across academia and industry. Development of expertise in cutting-edge experimental techniques, computational modelling, and interdisciplinary
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professional network spanning academia, industry, and national research centres. Through this multidisciplinary project, the student will develop expertise in: Contribute to the development and operation of
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part of a dynamic, focused and professional study environment and all become valued members of the Cranfield Doctoral Network. This network brings together both research students and staff, providing a
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into full-surface temperature profiles. Key outcomes include defining the required locations and extent of temperature monitoring to enable accurate data conversion. • Creating a practically deployable method
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, Meggitt, Thales, MOD, Bombardier, QinetiQ, Thales, Network Rail, Schlumberger and Alstom.
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of representative failure models for gear failures causes difficulties in their useful lifetime prediction. Critical operational parameters such as loading, speed and lubrication affect the physics of gear meshing