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within fusion reactors, especially plasma-facing materials (PFMs) exposed to intense heat fluxes and energetic particles. Understanding and predicting how these materials degrade under such conditions is
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of diagnostic and prognostic algorithms. Electronic Prognostics Systems: Facilities equipped to assess the health and predict the remaining useful life of electronic components, supporting studies in electronic
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systems that continuously assess the health of components, predicting failures before they occur. Compliance Assurance Techniques: Design AI-driven methods to ensure ongoing compliance with industry
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, embedded intelligence, and adaptive cyber-physical systems that operate safely under uncertainty and dynamic conditions. This PhD at Cranfield University explores the development of resilient, AI-enabled
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prognostic algorithms. Electronic Prognostics Systems: Facilities equipped to assess the health and predict the remaining useful life of electronic components, supporting studies in electronic system
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and misalignment, facilitating the development and validation of diagnostic and prognostic algorithms. Electronic Prognostics Systems: Facilities equipped to assess the health and predict the remaining
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performance assessments and ultimately reduce the differences between test-bed and in-flight conditions. This will be an enabler to de-risk the performance assessment of new aero-engines and contribute
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the future of Corrosion-Fatigue engineering. The application of repetitive loads results in fatigue damage that can propagate and cause catastrophic failures. The ambient conditions around a fatigue crack tip
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and design criteria to prevent hydrogen-induced failure in metallic aerospace materials. Through experimental testing of ferritic and austenitic steels under realistic service conditions, including
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UK student fee status only and are under no restrictions regarding how long they can stay in the UK. Sponsored by the EPSRC Centre for Doctoral Training in Net Zero Aviation and Rolls-Royce plc