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parallel expansion in mining to meet this demand is expected. The environmental, economic and social impacts of this global expansion are significant and need to be assessed, so that we do not trade one
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anthropogenic activities, as well as limitations of existing models in effectively integrating human data to quantify human influence. Foundation AI models offer significant potential due to their strength in
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information on this project and details of how to apply to it please visit https://centa.ac.uk/studentship/2026-b12-low-latitude-atmospheric-rivers-meteorology-forecasting-and-flood-risk/ Further information
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the rich, unstructured information found in clinical notes and cannot effectively gather data on lifestyle and social determinants of health. This PhD project will pioneer a novel, hybrid AI framework
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(synoptic scale) forecasting (< 5 days) to mid-century scenario-based climate predictions (time horizon 2030-2050). The project will explore the merging of data sources to estimate future energy requirements
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Intermittent streams (c. 50% of world rivers) and headwaters (c. 90% of rivers) are key riverine systems for hydrology, ecology and water quality, which are poorly monitored and lack data (Dugdale
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. Yet, many stellar and planetary parameters remain systematically uncertain due to limitations in stellar modelling and data interpretation. This PhD project will develop Bayesian Hierarchical Models
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, the project will integrate these data into a predictive framework to assess how MNPs disrupt plankton community dynamics and the efficiency of the biological carbon pump. The research will provide
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temperatures, with the former strongly correlated with surface ocean salinity. The project will build on existing post-expedition sample processing and pilot analyses. For further information on this project and
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for the next generation of models is the detailed treatment of spin precession and orbital eccentricity. These effects encode critical information about compact binary formation channels and evolutionary