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programming skills. Expertise in developing computer vision and machine learning algorithms would be desirable, highly motivated and enthusiastic about advancing AI for societal impact. Qualifications A high
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simulations are plagued by the same slow relaxational dynamics. Through collaboration across Engineering, Statistics and Chemistry, this project will develop state-of-the-art simulation algorithms to circumvent
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to maximise early outbreak detection. Active intervention: developing decision-making algorithms that recommend effective public-health interventions. Reinforcement learning (RL) provides a natural framework
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coupling, applications, and development of regional ocean models. Capitalising and contributing to this effort, this project will Investigate effective downscaling strategies for different regional ocean
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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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behaviour in a practical, real-time monitoring system requires advances in both sensor engineering and behavioural data interpretation. This PhD project aims to develop a next generation environmental
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volumes in a reliable, repeatable, and automated way. This project aims to establish a data-driven, adaptive framework that develops artificial intelligence tools, integrated with advanced geostatistics
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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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can take different paths depending on the candidate’s skills and interests. As well as receiving technical support and skill development from academic supervisors, you will receive input and direction
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Researcher will influence the direction of application areas and algorithm development, receiving direct training in InSAR processing, geospatial data science, and agricultural remote sensing. Co-supervision