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focuses on AI-driven fault diagnosis, predictive analytics, and embedded self-healing mechanisms, with applications in aerospace, robotics, smart energy, and industrial automation. Based
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to identify the material degradation and coatings applications details in extreme environments. A novel techniques/method will be developed with focus on better prediction and more accurate measurement of
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renewable energy, AI-driven engineering, and industrial research. Cranfield’s expertise in wind energy systems, predictive maintenance, and AI applications provides an ideal environment for cutting-edge
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. At a glance Application deadline01 Apr 2026 Award type(s)PhD Start date01 Jun 2026 EligibilityUK, EU, Rest of world Reference numberSATM606 Entry requirements Applicants should have an equivalent