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, current models only predict the potential for events rather than actual specific landslide occurrence. These models also struggle to directly quantify landslide hazards and to address key characteristics
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generative model-based domain translation, in collaboration with leading research institutions. This new studentship aims to develop the next generation of interpretable and cross-modal predictive models
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outcomes. By mapping these gene distributions and integrating them into a predictive tool, the project seeks to stratify patients as likely responders or non-responders to chemotherapy, enabling personalised
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either of these species is likely to affect its onward behaviour, and data on these processes will support predictive modelling. The PhD student will be a part of the Surrey/AWE Centre of Excellence in
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is essential for robust climate prediction and mitigation strategies. The tropical Atlantic is a pivotal region in the global methane cycle, where both methane sources and sinks are influenced by
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human behaviour, influenced by people’s social connections, and resources. Predicting disease spread is difficult due to factors like parent’s age, ethnicity, socioeconomic status, and nursery layout
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models. This theoretical project will facilitate close collaboration with experimental groups and enable benchmarking of theoretical predictions. The PhD researcher will be part of the Correlated Quantum
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UKHSA surveillance datasets to see if OTC sales can be used to monitor GI infection activity and better predict outbreaks. The PhD offers the unique opportunity to develop skills in analytical
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In this PhD project, we will develop and implement approaches for estimating the uncertainty in AI predictions of chemical reactivity, to help strengthen the interaction between human chemists and
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In this PhD project, we will develop and implement approaches for estimating the uncertainty in AI predictions of chemical reactivity, to help strengthen the interaction between human chemists and