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to predict coastal wetland restoration success. Successful candidate will first construct sensors using microcontrollers (e.g., Arduinos and peripheral sensors). These sensors will be designed to measure key
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of waterlogged conditions, peatlands are projected to be particularly impacted by future climate change, through changes in both temperature and precipitation. Bioclimatic envelope models predict significant loss
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predictive accuracy and prohibitively long computational times, making them unsuitable for real-time process control. Artificial intelligence (AI) models present a promising alternative by addressing
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guidelines don't fully account for human behaviour, influenced by people’s social connections, and resources. Predicting disease spread is difficult due to factors like parent’s age, ethnicity, socioeconomic
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models to predict defect behavior without the computational cost of DFT. The successful applicant should have or expect to achieve at least a 2.1 honours or equivalent at Bachelors or Masters level in
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microbial communities. In this role, you will develop hybrid species distribution models that combine climate and landscape data to predict how microbial taxa niches shift under changing land use and
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challenges to the electricity transmission and distribution system, as solar power is not dispatchable and therefore its incorporation as a major element of the generation mix requires the accurate prediction
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processes. Carbonate biomineralisation is a key process in global carbon cycling, but there are major gaps in our understanding of how biominerals form. We lack a quantitative understanding that can predict
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challenges to the electricity transmission and distribution system, as solar power is not dispatchable and therefore its incorporation as a major element of the generation mix requires the accurate prediction
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PhD Studentship: Optimisation of Liquid Metal Filtration and Cleanliness in Nickel Based Superalloys
computational research in Birmingham that has described and quantified the effects of oxides on material reliability, developed methods for prediction of their formation and rules for avoiding their occurrence