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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
mechanics, and artificial intelligence (AI)—specifically in the domains of non-destructive evaluation (NDE), computer vision, and machine learning. It addresses a critical challenge in the structural health
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on Artificial Intelligence (AI), Deep Reinforcement Learning (DRL), and Predictive Maintenance for optimizing wind turbine performance and reliability. This research will develop an AI-powered wind turbine
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for Security Operations Centres (SOCs) while pioneering strategies for quantum-era resilience. This project sits at the intersection of Artificial Intelligence, Cybersecurity, and Explainable Computing. It
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tuition fees. This PhD project in the area of autonomy, navigation and artificial intelligence, aims to advance the development of intelligent and resilient navigation systems for autonomous transport
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This project lies at the intersection of electronic hardware assurance, machine vision, and applied artificial intelligence, with a focus on non-destructive testing (NDT) techniques for complex
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conduct interdisciplinary research combining software engineering, artificial intelligence, IoT development, and human-computer interaction to create intelligent software systems that serve both technical
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-Royce, Thales, and UKRI—offering global relevance in low-power AI hardware, embedded intelligence, and adaptive electronics. The rapid advancement of Artificial Intelligence (AI) has necessitated
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should have a strong foundation in artificial intelligence, machine learning, and multi-agent systems, along with experience in programming, data analysis, and model development. Knowledge
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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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modelling, artificial intelligence, or marine operations. The project aims to develop a human-factor-informed simulated digital twin framework to assess technician welfare during offshore wind farm