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. If we receive a strong field of candidates, we may use the desirable criteria to choose our final shortlist, so please include your evidence against these where possible. To find out how our managers
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of the proposed replacement approaches will form an integral part of the proposed multi-objective optimisation approach. Such a methodology should help asset managers and system operators to make practical
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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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receive a strong field of candidates, we may use the desirable criteria for our final shortlist. To find out how our managers will review your application, take a look at our ‘ How we Recruit ’ pages.
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computational tools to support the safe and ethical deployment of AI in clinical settings. The research focus is on AI performance monitoring, distribution shift detection, bias assessment, and stress testing
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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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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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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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the detection of real versus AI videos. The focus has been from single disciplines (e.g., Masood et al., 2022), and typically technical in nature. By drawing on multiple modalities within video (i.e., visual
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on artificial turf surfaces. It will also benefit from a novel methodology to observe and measure the surface response during traction testing and it is expected that this will be further extended within the PhD