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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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operation of autonomous systems in complex, real-world conditions. This PhD project aims to develop resilient Position, Navigation and Timing (PNT) systems for autonomous transport, addressing a critical
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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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novel methodology for designing and assessing highly integrated fuel systems for ultra-efficient propulsion systems fuelled by SAF. The assessment will be done in terms of performance, operation and
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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