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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap
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thermodynamically. Performance design optimization and advanced performance simulation methods will be investigated, and corresponding computer software will be developed. The research will contribute
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Advances in computing, experiments, and information will continue to reshape engineering in the next decade. This PhD position will nurture a multidisciplinary innovator with the tools to unravel
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This self-funded PhD opportunity explores assured multi-sensor localisation in 6G terrestrial and non-terrestrial networks (TN–NTN), combining GNSS positioning, inertial systems, and vision-based
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This self-funded PhD opportunity focuses on assured multi-domain positioning, navigation, and timing (PNT), integrating data from space-based, terrestrial and platform-based sources of navigation
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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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This self-funded PhD opportunity sits at the intersection of several research domains: multi-modal positioning, navigation and timing (PNT) systems, AI-enhanced data analytics and signal processing
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of the overall efficiency of the system. Their degradation behaviour in different fuels (hydrogen, ammonia or bio-fuels) is yet to be understood. This PhD project aims to investigate the effect
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relevance. A digital twin framework for safe, simulation-based validation before deployment in operational wind farms. Develop explainable AI (XAI) frameworks and human-computer interfaces that enable wind
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-periodic structures, we can precisely control the interaction of radiation with matter, potentially achieving unprecedented timing resolution (sub-70ps) and significantly enhancing signal detection. This PhD