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University of California, San Francisco | San Francisco, California | United States | about 3 hours ago
up): TCS Non-Academic Titles Search (https://tcs.ucop.edu/non-academic-titles) Please note: An offer will take into consideration the experience of the final candidate AND the current salary level of
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Position Details Position Information Requisition Number Stu04895P Home Org Name Clinical Sciences Division Name College of Veterinary Medicine Position Title Student Worker - JTV Large Animal
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integration. • Experience developing dashboards, automated reporting, or large-scale administrative data integration. Preferred Qualifications • Master’s degree in in computer science, information systems, data
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). Incumbent will work closely with Senior Epidemiologist to develop analysis plans using data from a large clinical patient cohort. Ideal candidates will have expertise in designing and conducting
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data cabling installations for medium to large renovation or capital projects. Experience with design documents, equipment/material specifications, and development of statements of work. Demonstrated
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LevelPhD or equivalent Additional Information Work Location(s) Number of offers available1Company/InstituteCranfield UniversityCountryUnited KingdomGeofield Contact City Cranfield, Bedford Website http
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Deadline: none (posted 2026/01/21, updated 2026/01/20) Position Description: Apply Position Description Postdoctoral Associate in large-scale computing for water-energy systems Dr. Galelli’s Critical
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. Using functional specifications and designs, produces all or part of the deliverables. Maintains databases and application system code. Job Scope/Complexity Responsible for full life cycle of large/long
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accent change in large-scale real-time data from multiple generations in London, testing predictions of language contact and sociolinguistic theory, analysing the repertoires and play interactions of young
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practices in reproducible code (e.g. Git, notebooks, pipelines). Demonstrated experience in analyzing large-scale, high-dimensional datasets including multi-modal data integration and hands-on experience with