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predictive performance, computational efficiency, and spatial resolution through algorithm optimisation, tuning, and refined covariates. Assess trade-offs between spatial resolution and other performance
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using cutting-edge computational techniques, including machine learning algorithms. Work collaboratively with an interdisciplinary and international team to refine and validate regional wave and ocean
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of this research programme. The project will be the first of its kind to explore the validity of FIFA-approved skeletal tracking systems for the potential application in on-field gait analysis with the possibility
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to develop principled models and algorithms for distributed decision-making in complex and uncertain environments. Your research The candidate will develop a novel hierarchical control framework
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project aims to develop state-of-the-art computational methods to optimise the quality of doubly curved shell structures manufactured from recycled, short-fibre composites. A particular novelty of the
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) develop novel performance metrics combining accuracy and explainability, to be tested across different AI model types; (2) devise new algorithms for selecting models optimised for holistic performance
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identification context, while promising for network-level monitoring, has been largely underexplored. To this end, the project will explore the application of the next generation of deep learning algorithms, e.g
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, the project will develop algorithms for ecological sensing, adaptive motion planning, and energy optimisation under real-world constraints. Scaled experiments and high-fidelity simulations will validate system
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designed to meet multiple needs in marine biodiversity monitoring. The project aims to develop embedded novel deep learning and computer vision algorithms to extend the system’s capabilities to classify
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-weather perception for which Radar sensing/imaging is essential. This project focuses on developing algorithms, using signal processing/machine learning techniques, to realise all-weather perception in