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of a large multidisciplinary collaboration between LSHTM and Oxford Brookes University (lead partner), UCL, LSE, University of Leeds, University of Edinburgh, and a wide network of UK stakeholders
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techniques to enable large-scale data search across Personal Online Datastores (pods) hosted on distributed pod servers, addressing both keyword-based search and SPARQL querying. EPRESSO will build on and
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February 2025) that has mapped emotional hotspots in cities using spatial analysis of social media data. Our next effort aims to incorporate explainable AI (XAI) to interpret and explain spatial patterns
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different ML architectures for postprocessing precipitation forecasts over India. Determine how to maximise information extracted from the raw forecasts and how to optimise postprocessing skill for heavy
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to important public health topics. Studies will include descriptive epidemiology and use emulated target trial approaches for robust causal inference within large national health datasets. The post offers
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to data analysis and biological interpretation. Role Summary Develop, implement, and apply advanced reproducible computational tools and workflows to process, analyse, and interpret large-scale LC–MS-based
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of gender, political communication and “everyday sexism” Analysing and interpreting research findings and results based on large scale media data sets Contributing to engagement with non-academic partners, co
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(EMS) Developing a procedure for local manufacture of electrolyte Analysis and publication of data from the installed battery The successful candidate will work in collaboration with SOLead Energy, a
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a state-of-the-art Earth system models(UKESM). We are looking for someone experienced in analysing output from large, complex climate models or Earth system datasets, and who is experienced in or keen
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complementary. Broadly the posts include a qualitative research methods expert, a qualitative research methods expert in big data, and a quantitative research methods expert. Please note that this post may be