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this issue and we could use obtain data-driven models using machine learning algorithms such as artificial neural networks, reinforcement learning, and deep learning. A typical caveat of data-driven modelling
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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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, supporting studies in military aircraft systems. Engaging with these facilities allows students to acquire practical skills and technical expertise, enhancing their research capabilities and employability in
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the intersection of ecology, machine learning, and sustainable land management, the research will combine field data collection, deep learning model development, and stakeholder co-design to support biodiversity
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intelligence, multi-agent systems, and the design of AI models. They will also acquire transferable skills in interdisciplinary problem-solving and innovation, which will significantly enhance
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diagnosis and prognosis technologies, and, consequently, improve maintenance decision making. Currently, machine learning exists as the most promising technologies of big data analytics in industrial problems
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systems safer, more efficient, and more sustainable. The aim of this project is to design a smart cognitive navigation framework that information from various sensors and learn to make decisions on its own
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industrial partners, such as WAAM3D (https://waam3d.com/ ) and members of WAAMMat (https://waammat.com/ ), gaining valuable industry experience and exposure. The student is expected to acquire the following
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that conduct research with academic leaders across leading UK institutions. Engage in online and face-to-face activities, including cohort-building events and collaborative learning exercises • Funding: A
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of WAAMMat (https://waammat.com/ ), gaining valuable industry experience and exposure. The student is expected to acquire the following (including but not limited to) knowledge and skills from the research in