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implemented in the Fortran programming language, and it relies on the platform CUDA for parallelization of the computation over several GPUs’ cores, and has interfaces with Matlab and Python for ease of use
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well as market and organization considerations. Education: Ph.D. in machine learning, computer science, engineering, science or related technical discipline. Experience: Expertise in developing and training AI
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program of independent research with the primary goal of improving the algorithm codes used to characterize the Earth’s gravitational field. Duties will include specialized scientific/numerical analysis
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with a previous background on GPU computing are especially encouraged to apply. However, other research profiles, within the realm of computation physics, especially with a background in strongly
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Since 2006, the University of Luxembourg has invested in its own High-Performance Computing (HPC) facilities. A special focus was placed on developing large computing power combined with massive
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libraries for modern architectures (e.g., GPUs). Exploration of linear algebra methods in computational physics applications and machine learning. Integrate and benchmark the GINGKO library, a sparse solver
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Since 2006, the University of Luxembourg has invested in its own High-Performance Computing (HPC) facilities. A special focus was placed on developing large computing power combined with massive
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Post-doctorate position (M/F) : Exascale Port of a 3D Sparse PIC Simulation Code for Plasma Modeling
degree / PhD computer science or physics with high-performance computing - Experience in Fortran, C or C++ programming - Experience in high-performance computing and parallel programming, in particular GPU
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MPI Understanding of plasma physics and computational plasma physics Experience with Particle-In-Cell (PIC) code development and kinetic models Experience in high-performance computing (HPC) and/or GPU
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Computer Science, Applied Mathematics, Physics, Computational Biology, Neuroscience with Computational or Theoretical focus, or a closely related field. Preferred Qualifications: Familiar with Information Theory