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benchmark them with a realistic case study. The main focus of the project can develop either more in the mathematical theory of MCMC, the implementation of code for the Jülich supercomputers (GPU/CPU
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will be considered a plus: Data analysis libraries such as Dask Knowledge and experience with Python, Fortran and/or GPU computing HPC and parallel libraries such as OpenMP and MPI HPC parallel IO
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and client-based firewalls. General hardware troubleshooting and repair skills. Experience supporting the installation, configuration, and monitoring of Nvidia GPU cards and required drivers and
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and client-based firewalls. General hardware troubleshooting and repair skills. Experience supporting the installation, configuration, and monitoring of Nvidia GPU cards and required drivers and
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troubleshooting and repair skills. Experience supporting the installation, configuration, and monitoring of Nvidia GPU cards and required drivers and software within Linux. Experience supporting researchers in
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cluster & GPUs — and deep integration with clinical data including electronic health records A diverse array of ongoing research, education, patient care, and community centered activities that require
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the following is preferred: Training and optimizing ML algorithms on GPU hardware architectures, specifically NVIDIA based Statistics, linear algebra, and data structures Exploratory data analysis and
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08.04.2022, Wissenschaftliches Personal Development of Lattice-Boltzmann solver, programming in C/C++ and CUDA, implementation on GPU cluster, testing of real-time capable software on flight
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tuned for thermal transport and fire dynamics. FDS has been scaled up to 10,000 cores on Titan. The aim of this project is to improve code performance for serial, OpenMP, MPI, and potentially GPU
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of microfluidic devices. Simulation for microfluidics. (CFD) High Performance Computing and/or GPU programming for this domain. Machine learning algorithms for this domain Clean energy solutions (e.g., microfluidic