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optical transition and favorable spin properties of individual solid-date erbium ions (Er3+) to store quantum information necessary for practical, robust, and scalable quantum communication. The focus
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with this group to evaluate AERIS at S2S scales, couple ocean component to the model, data assimilation and regional refinement. In particular, this position will utilize generative AI to create a
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bioreactors; collect, analyze, and interpret biological, chemical, and microbial omics data; and integrate results to evaluate process performance and scalability. The candidate will contribute to process
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design, development of supervisory control logic, validation of vehicle models against test data etc. The projects and interests of this group span all modes of transportation including off-road, rail
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will receive full consideration. Key Responsibilities AI-ready data and analysis for the ePIC Barrel Imaging Calorimeter and our Jefferson Lab program Support for the PRad-II and X17 experiments
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the ability and motivation to develop expertise in large-scale model training and scaling on HPC systems, as well as in handling the unique characteristics of scientific data, including large-scale numerical
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cryogenic environments Participate in synchrotron-based characterization and data analysis Contribute to high-impact publications, internal reports, and scientific presentations at conferences and workshops
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on the development of the SPHEREx Legacy Galaxy Clusters Catalog. The successful candidate will lead analyses to characterize galaxy populations in clusters using SPHEREx data in combination with complementary wide
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data processing and analysis techniques is a plus Strong written and oral communication skills Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork Candidates with a
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optical and THz techniques. Ability to analyze and understand complex data set is required. Experience to lead ultrafast x-ray scattering or electron scattering experiments is a plus but not required