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This PhD at Cranfield University explores the development of resilient, AI-enabled electronic systems capable of detecting faults and autonomously recovering from failures in real time. The project
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Hardware Security and Embedded Resilience – Explore AI-powered hardware security primitives, anomaly detection, and self-healing mechanisms for IoT, cyber-physical systems, and mission-critical applications
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compliance and operational integrity. The application of AI in these areas enhances the ability to predict system behaviours, detect anomalies, and streamline certification workflows. AI-driven tools can
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to discover the fundamental mechanisms responsible for damage and deformation. About the host University and Through-life Engineering Services (TES) Centre Cranfield is an exclusively postgraduate university
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involves feeding a metal filler wire, either coaxially or off-axis, into an electric arc to create a molten pool that solidifies on a substrate, enabling the layer-by-layer construction of 3D objects
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. The enhanced image quality will support earlier and more reliable detection of eye diseases. Combining artificial intelligence with mathematical modelling, this non-invasive, cost-effective approach has
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Degradation Faults: Simulates various degradation scenarios in unmanned aerial vehicle (UAV) fuel systems, enabling research into fault detection, isolation, and prognostics. Machine Fault Simulator
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research into fault detection, isolation, and prognostics. Machine Fault Simulator for Rotating Machinery Faults: A versatile platform that replicates common faults in rotating machinery, such as imbalance
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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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electronics, embedded programming, signal processing, vibration measurement and analysis, maintenance engineering, and electro-mechanical engineering. Funding This is a self-funded PhD. Find out more about fees