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CFD technologies. As the PhD researcher on this project, you will investigate and develop the numerical and algorithmic components needed to make this hybrid high order to low order strategy practical
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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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modelling becomes crucial for developing effective mitigation and adaptation strategies for marine infrastructure. Within the Institute of Infrastructure and Environment, we maintain a track record in the
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Department and aims to develop on-going work on the decision support system. Project aims and objectives This project aims to develop new, scientifically valid applications of skeletal data extracted from
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allowing elements to span across multiple CAD faces without explicitly modifying the geometry. However, these ideas have not yet been developed in high-order settings, where curved elements, geometric
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data are needed to enhance our understanding of sources, pathways and impact of litter. Cefas is developing a visible light (VL) deep learning (DL) algorithm and collected a large 89 litter category
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will include race videos, rider power and speed data, and race commentary to codify key race events, using expert knowledge and available evidence. - Develop a post-race analysis framework, process, and
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adaptation of the mesh during simulation to resolve and track features in the flow. The focus of your PhD would be on developing novel algorithms to efficiently redistribute and rebalance the parallel
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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