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and optimization strategies for large-scale or streaming data. Develop parallelized and GPU-accelerated learning modules, ensuring scalability and performance efficiency. Build and maintain robust data
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between subdomains. You will also develop and optimise parallel algorithms for large scale execution on HPC systems, contributing MPI, OpenMP and memory efficient components with strong emphasis
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(parallelization, efficient data structures), numerical testing, and results analysis. Familiarity with numerical methods, scientific programming in C++, and an interest in reservoir engineering problems
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 5 hours ago
approaches include MHD, Vlasov, and hybrid codes run on computers ranging from PCs to massively parallel computers, while analytical work involves linear and nonlinear instability theories. Location: Goddard
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characterize microplastic contamination in urban environments and to estimate the annual microplastic loads discharged into receiving rivers at the scale of a given territory. In parallel, the postdoctoral
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conservative transmission algorithms that ensure consistent exchange of information between subdomains. You will also develop and optimise parallel algorithms for large scale execution on HPC systems
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develop computational fluid dynamic (CFD) tools that make exascale computing accessible to a broader set of users. The successful candidate will develop a massively parallel solver, capable of running
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and optimization strategies for large-scale or streaming data. Develop parallelized and GPU-accelerated learning modules, ensuring scalability and performance efficiency. Build and maintain robust data
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- Knowledge and experience with Python, Fortran and/or GPU computing - HPC and parallel libraries such as OpenMP and MPI - HPC parallel IO libraries such as HDF5 or NetCDF - Experience with supercomputer tools
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algorithms in the context of sparse optimization and applications in real world data sets. We are also interested in exploring opportunities for parallelism of the completion process, highlighting