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mission. You will: Help collate data resources relevant to suicide and self-harm. Develop new machine learning methodologies (from artificial neural networks, decision trees, evolutionary algorithms and
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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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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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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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) 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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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
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to train tomorrow’s leaders in earth and environmental science. For further details about the programme please see http://nercgw4plus.ac.uk/ For eligible successful applicants, the studentships comprises
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. Exposure to neural-symbolic algorithms for transforming intent into conformant security or safety policy and/or enforcing security controls is optional but beneficial. Research will also give the opportunity
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implementing AI algorithms to deliver safer and more efficient care. The student will have access to a unique training programme in AI in healthcare and health data science as well as a wide range opportunities
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representation. Key aims include improving the generalizability, interpretability, reasoning and causal grounding of these models, developing new optimisation algorithms with biologically meaningful regularisation