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Species’ distributions are shifting in response to global climate change and other human pressures. Accurate methods to monitor and predict distribution shifts are urgently needed to manage
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-aware models and schemes for optimising distribution, storage and processing of tasks and data in each layer.
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, software, human-computer interaction, ...). We also work very much interdisciplinarily with colleagues from other faculties, e.g. on bio-diversity matters, on physical aspects, on modelling aspects, and on
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AI is now trending, and impacting diverse application domains beyond IT, from education (chatGPT) to natural sciences (protein analysis) to social media. This PhD research focuses on the fusing AI
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". A number of emerging approaches, such as zero resource and unsupervised NMT, have investigated alternative methods in developing NMT models where sufficient parallel corpora are not available (eg [1,2
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catastrophically so. This PhD will develop technologies for addressing this serious problem, building upon our groundbreaking research into the problem. Required knowledge A solid grounding in machine learning
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strong out-of-distribution generalization capability [2]. If user-specific information is identified and removable from the input data, the devised techniques can also be applied for privacy-sensitive
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Visualisation research; there are so many interesting experimental research questions to address that researchers seldom wish to repeat work that they (or others) have already completed and published. This PhD
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these challenges, calling for the development of responsible AI systems that are transparent, trustworthy, and aligned with human values in educational contexts. This PhD project aims to design, develop, and
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Skip to main content Main Menu - Primary Home Projects Supervisors Expression of Interest Contact Computational drug discovery Primary supervisor Geoff Webb Research area Data Science and Artificial