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unit and then pre-processed data used as the input of the deep learning algorithm. The research will employ the SafeML tool (a novel open-source safety monitoring tool) to measure the statistical
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health. Policymakers allocate limited testing and surveillance resources across different locations, aiming to maximise the information gained about disease prevalence and incidence. This project will
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. Design and implement multimodal unlearning techniques to address bias and privacy concerns. Evaluate the generalisability of multimodal learning across different socio-contexts. Validate the proposed
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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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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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expand current technology to include automated live analysis, integrating machine learning algorithms capable of interpreting the complex behavioural patterns of mussels in response to environmental stress
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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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provide large and complex datasets. By applying advanced pattern recognition and clustering algorithms, the aim is to automatically detect coherent spatial domains. These domains represent regions with
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Museums and Inclusive Heritage Preservation Platform Labour, Creator Economies, and Algorithmic Change AI in the Creative Industries (cross-faculty potential) Independent Cinema Exhibition and UK Screen