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conditions. Our work combines traditional statistical methods with advanced artificial intelligence algorithms to identify patterns in disease. We also use qualitative methods to understand lived experiences
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. The posts will involve working across multiple project grants aiming to evaluate the use of wearable technology in laboratory and real-world settings to quantify digital mobility outcomes in ageing and
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integrates machine learning and statistics to improve the efficiency and scalability of statistical algorithms. The project will develop innovative techniques to accelerate computational methods in uncertainty
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learning (ML) for high-fidelity data ‘stitching’. The integration of data from multiple analytical platforms is critical for advancing the understanding of complex biological and chemical systems. This work
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for greater precision. Machine learning (ML) algorithms will analyse these datasets to deliver a scalable, cost-effective system, validated through field trials and enhanced by contributions from four
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algorithm, duality theory, and sensitivity analysis; Optimisation, focusing on single-variable optimisation, constrained optimisation involving non-linear objective functions with multiple variables, and the
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electronic converters are required to connect renewable energy sources and energy storage systems to the power network. These converters employ sophisticated control algorithms that must simultaneously achieve
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to apply it in selected poor-resource settings. This project aims to achieve several objectives, including the development of a new AI-algorithm and a paired dataset for comparing how different imaging
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Stack Software Developer. The group aims to identify, understand, and develop therapies for rare genetic disorders. The group is primarily computational but partners with multiple international labs
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Stack Software Developer. The group aims to identify, understand, and develop therapies for rare genetic disorders. The group is primarily computational but partners with multiple international labs