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? Other EU programme Reference Number https://cnes.fr/theses-post-doctorats Is the Job related to staff position within a Research Infrastructure? No Offer Description The call for applications will be open
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NAISS, the National Academic Infrastructure for Supercomputing in Sweden, provides academic users with high-performance computing resources, storage capacity, and data services. NAISS is hosted by
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Great Falls College Montana State University | Great Falls, Montana | United States | about 23 hours ago
literacy, including Microsoft Office software and education specific software applications The Successful Candidate Will Special Requirements Physical Demands Must be able to perform all duties as allowed by
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, giving rise to complex circuits with often highly branched topologies. The research and technologies thus developed address both new energy management functionalities and high-performance applications in
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 22 hours ago
Position Information General Information Position Number STMP15 Working Title Program Coordinator, Corporate Training & Executive Education Division Academic Affairs Department School
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to de-risk high magnetic field operation of HTS. The prototype HTS cable and coil module will be tested in a background magnetic field test facility in PPPL. The job involves conductor characterization
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of the University of Illinois at Urbana-Champaign. Job Summary Provide high quality interactive classes within the community and provide continuous program information and interaction between university staff and
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Clinical Laboratory Improvement Amendment (CLIA) compliant standard operating procedures, tests and methods in the Nebraska Public Health Laboratory (NPHL). Sets work schedules, performs quality control
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resources (i.e., engaging vendor support) to provide a high level of technical support to clients. Job Description Primary Duties & Responsibilities: Desktop support: Provides daily support for the computing
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surrogates or approximators, such as random forests or shallow neural networks, trained to mimic the outputs of the original computations at a fraction of the cost. This hybridization aims not only