65 cloud-computing-adoption Postdoctoral positions at Oak Ridge National Laboratory in US
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interact closely with industry partners. These engagements will play a vital role in ensuring success of programs and the adoption by project sponsors, and in developing your network across academia and
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computing, with a particular emphasis on methods tailored to study nonequilibrium quantum many-body systems. The position offers an exciting opportunity to contribute to cutting-edge research
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in ORNL’s Center for Radiation Protection Knowledge (CRPK). The candidate will work with experts in computational radiation dosimetry and risk assessment. The candidate should be an independent thinker
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interact closely with industry partners. These engagements will play a vital role in ensuring success of programs and the adoption by project sponsors, and in developing your network across academia and
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Requisition Id 16765 Overview: The National Center for Computational Sciences (NCCS) at the Oak Ridge National Laboratory (ORNL) is seeking a postdoctoral research associate in the area of HPC
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Requisition Id 16262 Overview: We are seeking a postdoctoral researcher to work at the intersection of tensor networks, quantum algorithms, scientific computing, topological physics, and quantum
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-time data acquisition and telemetry systems Familiarity with cloud computing platforms and edge deployment of ML models Experience with uncertainty quantification, sensitivity analysis, or robust
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platform. The successful candidate will (1) integrate heterogeneous sensors and onboard computing hardware; (2) develop methods for LiDAR-based simultaneous localization and mapping (SLAM), autonomous
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Requisition Id 17066 Overview: The Computational Hydrology and Atmospheric Science (CHAS) Group within the Computational Sciences and Engineering Division (CSED) at Oak Ridge National Laboratory
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at scientific edge systems using large-scale HPC/AI computational and storage systems. Design and evaluation of ephemeral, user-configurable, and composable data and storage systems. Evaluation of cloud data