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a particular emphasis on error-corrected methods for future fault-tolerant quantum computing. The algorithms will be designed to address key models of quantum materials, such as the Hubbard model
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Requisition Id 16167 Overview: The Multiphysics Modeling and Flows (MMF) Group in the Computational Sciences and Engineering Division is seeking a Postdoctoral Research Associate with expertise in
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power electronics resources modeling, explore different intelligence algorithms to enhance ease of usage of simulations, and different applications of EMT simulations. Selection will be based
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Ph.D degree in electrical engineering, computer engineering, computer science, or a related discipline Demonstrated experience developing, training, and applying AI algorithms to physical sensor data
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learning algorithms for engineering systems Programming experience in FORTRAN, C, or C++ and scripting experience in Python or similar languages Experience with parallel computing environments and Linux
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intelligence, and architecture-aware algorithms that harness machines, ideas, and data to enable far reaching scientific breakthroughs. We develop computer innovations and advanced practical tools to address
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to Computational Methods for Data Reduction. Topics include data compression and reconstruction, data movement, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a
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Oak Ridge National Laboratory, Mathematics in Computation Section Position ID: ORNL-POSTDOCTORALRESEARCHASSOCIATE5 [#27233] Position Title: Position Location: Oak Ridge, Tennessee 37831
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Oak Ridge National Laboratory, Mathematics in Computation Section Position ID: ORNL-POSTDOCTORALRESEARCHASSOCIATE3 [#27208] Position Title: Position Type: Postdoctoral Position Location: Oak Ridge
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development of novel AI algorithms, it does require strong implementation skills, sound statistical judgment, and an ability to translate methods into reliable, maintainable, and well-documented pipelines