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, a state-of-the-art process-based model for groundwater risk assessment and contaminant transport modeling. By improving predictive modeling of transient contaminant source terms, this research will
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will also include evaluating and validating existing numerical models, ensuring their reliability in predicting real-world conditions. This project is supported by brand-new laboratory facilities
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antibiotic mixtures across temperature scenarios in diverse freshwater bacteria. This research aims to improve predictions of how combined stressors influence bacterial communities and broader ecosystem
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, the project will compare trends in OTC medication sales to other UKHSA surveillance datasets to see if OTC sales can be used to monitor GI infection activity and better predict outbreaks. The PhD offers
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this, there are further sub-objectives during the investigation to achieve this goal: Predict thermal warpage effects on a supersonic intake at different flight times, coupled to a numerical model for the downstream
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effect can be predicted. You will acquire in-situ and remote-sensing data of cirrus forming downwind of flights over the past decade, along with measurements/estimates of local conditions and emissions
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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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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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perpetuation (or maintenance/persistence); to build ML models that include the heart’s physical properties to find patterns in the data and predict which areas in the heart drive AF. This project will explore