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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 Applicants should have a first or
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Warfare, Information and Cyber (EWIC) provides an exceptional environment for this research, offering access to specialised expertise, cutting-edge labs, and a network of defence and security partners
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of the student’s project and allow them to network. The partners will also be able to offer the PhD student the opportunity of placements, during which the student can gain major insights. This would help them
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intensive training in energy modelling, AI-accelerated optimisation, and lifecycle-aware computing. Whether working on smart mobility, sensor nodes, or autonomous platforms, you’ll be contributing to a new
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to arrange the tuition fees and living expenses. Find out more about fees here . Cranfield Doctoral Network Research students at Cranfield benefit from being part of a dynamic, focused and professional study
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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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. 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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materials science and hydrogen technologies. The industrial sponsor, Airbus, is committed to net zero aviation by 2050 and is pioneering LH2 powered aircraft. This partnership provides a unique industrial
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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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-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