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Early and accurate cancer detection remains a critical global healthcare challenge, with profound implications for patient outcomes and treatment strategies. While Time-of-Flight Positron Emission
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Early and accurate cancer detection is a major global healthcare challenge, with significant implications for patient outcomes and treatment strategies. Time-of-Flight Positron Emission Tomography
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
techniques (e.g., ultrasonic C-scan, X-ray CT, thermography) rely heavily on expert interpretation, are time-consuming, and often fail to detect subsurface or latent damage accurately. Advances in artificial
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innovation and find diverse applications across industries such as aerospace, energy, and automotive. Among its various techniques, wire-arc directed energy deposition (WA-DED) stands out as a highly promising
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of the challenges is fault detection and diagnosis of bearings subject to low (rotational) speed. As vibration/acoustic signals generated by the faults of low-speed bearings are very weak and often covered by strong
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
engineers detect faults earlier, track system degradation, and make better-informed maintenance decisions. But how can we turn this complex information into something reliable, explainable, and actionable
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trust in digital communications and readily bypass conventional security controls. This PhD research proposes to design, develop, and validate a novel, explainable, multi-modal detection framework. By
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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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control system that enhances Annual Energy Production (AEP), reduces mechanical stress, and improves fault detection using machine learning (ML) and physics-based modelling. The candidate will gain hands
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. The project delves into areas such as hardware-based security measures, tamper detection, and the integration of explainable AI models within embedded platforms. Situated within the esteemed IVHM Centre and