51 high-performance-computing-"Study-Group"-"Study-Group" Postdoctoral positions at Oak Ridge National Laboratory
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Oak Ridge National Laboratory, Mathematics in Computation Section Position ID: ORNL-POSTDOCTORALRESEARCHASSOCIATE2 [#27206] Position Title: Position Location: Oak Ridge, Tennessee 37831
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Research Associate to develop and apply computational technique for advanced manufacturing using high-performance computing resources. ORNL’s CCP conduct world-leading research and development in multi-scale
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to numerical methods for kinetic equations. Mathematical topics of interest include high-dimensional approximation, closure models, machine learning models, hybrid methods, structure preserving methods, and
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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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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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-resolution microscopy, and in situ neutron or X-ray scattering and tomography methods. Strong background in computational and image-processing software, scientific programming, and high-performance computing
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, Computer Science, or a closely related field. Experience in at least one of the following areas: FPGA programming (VHDL/Verilog, HLS) Pixel detectors in high-energy physics or radiation detection
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in high-performance computing and data analytics with applications in a large variety of science domains and NCCS is home to some of the fastest supercomputers and storage systems in the world
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advanced many-body methods, high-performance computing, and machine learning approaches. The successful candidate will play a leading role in developing computational methods and high-performance algorithms
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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