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Requisition Id 15602 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
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physics (HEP) detectors, neuromorphic computing, FPGA/ASIC design, and machine learning for edge processing. The successful candidate will work with a multi-institutional and multi-disciplinary team
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Requisition Id 15823 Overview: We are seeking a postdoctoral researcher skilled in biogeochemistry who will contribute to mercury remediation technology development program, specifically focusing
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scientific papers in key journals and present at key meetings. Ensure compliance with environment, safety, health, and quality program requirements. Maintain a strong commitment to the implementation and
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.) to enable real-time process monitoring of the Directed Energy Deposition (DED) printing process. A background in sensors, instrumentation, and data analytics is preferred. Strong communication and program
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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
that can incorporate multi-scale computational simulations to aid with data fusion across multiple modalities of experiments with the final goal of discovering novel materials phenomena or even new materials
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liquids, frustrated magnetism, excitonic magnets, and strongly correlated electron systems. You will work closely with theorists, experimentalists, and computer scientists to build robust, scalable
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Oak Ridge National Laboratory, Mathematics in Computation Section Position ID: ORNL-POSTDOCTORALRESEARCHASSOCIATE1 [#27205] Position Title: Position Type: Postdoctoral Position Location: Oak Ridge
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designated position. WSAP positions require passing a pre-placement drug test and participation in an ongoing random drug testing program. HSPD-12 PIV badge: This position requires the ability to obtain and
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to Computational Fluid Dynamics. Mathematical topics of interest include structure-preserving finite element methods, advanced solver strategies, multi-fluid systems, surrogate modeling, machine learning, and