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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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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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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
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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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through advanced modelling and simulation. A key objective is to validate and optimize poroelastic finite element models of brain tissue, making them more accurate and clinically relevant. Additionally
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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