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programming proficiency in Python and C/C++. Expertise in ensemble learning (e.g., Random Forests, Gradient Boosting, bagging/stacking frameworks). Hands-on experience with parallel or GPU-based computing (CUDA
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 44 minutes 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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16 Jan 2026 - 23:59 (UTC) Type of Contract To be defined Job Status Not Applicable Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to
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hybrid discretisation frameworks that apply varying fidelity across the computational domain. This includes creating conservative transmission algorithms that ensure consistent exchange of information
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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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evaluate numerical methods that combine low and high order formulations, and develop hybrid discretisation frameworks that apply varying fidelity across the computational domain. This includes creating
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programming proficiency in Python and C/C++. Expertise in ensemble learning (e.g., Random Forests, Gradient Boosting, bagging/stacking frameworks). Hands-on experience with parallel or GPU-based computing (CUDA
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. Previous experience in computational modeling of atmospheric aerosols and parallel computing/software development is strongly desired. The term of appointment is based on rank. Positions at the postdoctoral
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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 tensor operations and apply them to real-world datasets. Parallel Computing: Explore opportunities for parallelism in the tensor completion process to enhance computational