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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 extraction, natural language generation, and text summarisation. Professor Bontcheva is the coordinator of FP7 PHEME project, scientific coordinator for H2020 WeVerify project, USFD PI for the EDMO
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Three 3.5 year PhD studentships in artificial intelligence in medicine or health data science funded by the National Institute for Health Research UCL UCLH Biomedical Research Centre are available
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equivalent. Further information: https://centa.ac.uk/apply/ International students are eligible for studentships to a maximum of 30% of the cohort. Funding does not cover any additional costs relating
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costs the NHS over £1 billion annually in England and Wales. Despite this, routine pollen data remain limited in resolution and frequency, underscoring the need for retrospective datasets to support
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Project Supervisors: Dr Pedi Obani Project Description: Climate change makes existing problems worse, especially for already vulnerable and marginalised groups. Unfortunately, data about climate
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with the world’s highest volcanic activity, yet eruption histories, geological maps, and monitoring data are incomplete or absent. Volcanoes capable of very large magnitude eruptions (M ≥ 6), with
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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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having been used by humans and integrated the data with a global bivalve database of species traits, fossil occurrences and geographic distributions, setting the foundation for a forecasting framework
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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