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established classical protection schemes with data-driven methods, including artificial intelligence and machine learning. The proposed protection strategies are expected to exhibit the following key attributes
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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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refinement or a loss of fidelity in critical regions. Machine learning provides a promising route to capture these relationships more systematically by identifying how local geometric features determine the
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. Current XAI methods are often generic and overlook industrial realities. This project will embed user-centric explanations directly into machine learning workflows using structured, ontology-driven
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Wales, meaning most paediatric records are handwritten and unstructured. The project will prioritise digitising these records using natural language processing (NLP) and machine learning (ML) to create
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, programming, signal analysis or machine learning are particularly valuable. If you are keen to apply technology to improve global healthcare, we would be delighted to hear from you. Entry requirements
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of separating fire-induced signatures from natural environmental variability (weather, canopy changes, tree motion) and fluctuations in the SoO sources themselves. Machine-learning methods will help improve long
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industry and academia together to drive pre-competitive, fundamental research in polymers. We welcome applicants with interests in polymer physics, materials processing and characterisation, machine learning
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-grade experience that employers value. The journey You'll develop machine-readable privacy rules, build core functionalities that audit and explain data-sharing decisions, prototype agent systems showing
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on combining innovative technologies such as remote monitoring, large language models, machine learning, blockchain, and eco-accounting to enhance the efficiency, security, and sustainability of e-bike charging