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results. Machine Learning skills to automise comparison process. Unbiased approach to different theoretical models. Experience in HPC system usage and parallel/distributed computing. Knowledge in GPU-based
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hydrodynamics and/or N-body simulations in the star and planet formation context Experience in the field with HPC system usage and parallel/distributed computing Knowledge in GPU-based programming would be
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finite elements) as well as alternative discretization methods (e.g., Lattice Boltzmann Methods), and high-performance computing. A selection of possible research areas can be found on our website: https
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with GPU-accelerated computation and high-dimensional data analysis. Enthusiasm for applying AI innovations to real biological and medical challenges. Required Application Materials: Cover letter
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conferences. Qualifications: PhD in computer science with file systems, GPU architecture experience. Proven ability to articulate research work and findings in peer-reviewed proceedings. Knowledge of systems
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 19 hours ago
cloud compute credits are available along with CPU/GPU nodes provided by the university. The expected outcomes of the position are: lead 1-2 major projects, contribute to other projects, collaborate with