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environments like health care and environmental monitoring. This PhD project aims to address these challenges by exploring how evolutionary algorithms and reinforcement learning (RL) techniques can be combined
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environments like health care and environmental monitoring. This PhD project aims to address these challenges by exploring how evolutionary algorithms and reinforcement learning (RL) techniques can be combined
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will develop and evaluate new approaches to predicting current and future population exposure to such hazards by combining numerical modelling and remote sensing of river migration, with machine learning
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role in the University’s Science Strategy and have an instrumental part in research projects that have a significant impact on both your chosen field of study and wider society. We combine leading
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. This will enable generation of real-time alerts and warnings, allowing for rapid response to pollution events or the onset of HABs. The technical innovation lies in combining robust low-power hall-sensor
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, developing spatial statistical models, and translating results into actionable insights for policy and adaptation. The strength of the project lies in its interdisciplinarity, combining atmospheric science
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allowing us to recognise individuals on the basis of visual and vocal characteristics and automate the large-scale quantification of behavioural and physiological responses. The aim of this PhD is to combine
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the evolutionary forces maintaining genetic variation in disease susceptibility in wild populations of bank voles (Myodes glareolus). By combining whole-genome re-sequencing of host populations with systematic
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will investigate how microbiome composition influences the physiological performance of bivalves under environmental stress. Combining microbial community profiling with measures of animal physiology and