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for conducting quantitative data analysis of large secondary datasets and be based at the University of Bristol. There are opportunities to work on several other ongoing and upcoming research projects to help
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the use of large administrative data to understand and address inequality. OI has published seminal work on national trends in upward mobility ; racial disparities ; the role of neighborhoods , social
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expertise in life cycle assessment, soil science, and big data analytics. This role offers you the chance to directly impact global food security and environmental sustainability while working in a
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internationally recognized leader in the science of economic mobility and in the use of large administrative data to understand and address inequality. OI has published seminal work on national trends in upward
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outputs across Projects 3-1 and 3-2 Manage and process large sets of operational data for case study analysis Contribute expertise in power grid optimisation to enhance planning models Participate in
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interdisciplinary team at the Technische Universität Dresden, focusing on the integration of multi-omics data to better understand, diagnose, and treat metabolic diseases (i.e. obesity, diabetes, metabolic
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Engineering or similar discipline. Preferred skills: Linux operation and big data Surface hydrology background. A minimum GPA of 3.0. Application Requirements A complete application consists of: Zintellect
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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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responsibility for: Designing, implementing, and overseeing large-scale team projects, including multi-year projects, in collaboration with Analysis and Data teams to ensure projects support CSET's data-driven
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using large language models, disease endpoint coding initiatives, and creation of common data model (CDM). He/she will also be expected to support/lead high-quality research in chronic disease