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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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modelling software. Practical experience in advanced manufacturing techniques for novel materials. Opportunities to present research at international conferences and build a professional network across
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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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to industrial clients such as Boeing, BAE Systems, Rolls-Royce, Meggitt, Thales, MOD, Bombardier, QinetiQ, Thales, Network Rail, Schlumberger and Alstom. Entry requirements A minimum of a 2:1 first degree in a
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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
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graphene and nanoparticles synthesis and its industrial applications in engineering sectors. Cranfield Doctoral Network Research students at Cranfield benefit from being part of a dynamic, focused and