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(e.g., MPI, OpenMP, CUDA) and high-performance interconnects (e.g., InfiniBand). Preferred Qualifications: Familiarity with advanced storage solutions and parallel file systems (e.g., Lustre, GPFS
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techniques and probability theory ● GPU Programming: Experience with GPU programming and optimization for ML models, utilizing frameworks, like CUDA or OpenCL ● Experience with applied computer vision, such as
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mathematics, e.g., probability theory, linear algebra, differential/integral calculus Prior programming experience in Python is a must, C++ and CUDA experience are a plus Hands-on experience in working with
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-performance computing, including parallel or GPU programming (MPI, OpenMP, CUDA, Kokkos, etc.) Familiarity with modern software development practices, including debugging, profiling, and version control
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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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WEKA, VAST, GPFS, BGFS, CEPH. Experience with installing and supporting: Open source and commercial research related software, Python, R, Matlab, Mathworks, Julia, Ansys, Intel, nVidia CUDA and GCC
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-performance computing Experience in design of computational pipelines for large-scale imaging Experience with programming languages and scripting methods (i.e. Python, MATLAB, C++, CUDA, Bash, and/or SQL) and
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Engineering, or a related field Strong experience in building and optimizing AI systems using PyTorch, TensorFlow, or JAX Practical knowledge of NVIDIA GPU programming (CUDA) and experience with inference
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Strong foundation in CFD, Programming proficiency such as Python, AI/ML techniques, Experience with parallel computing on CPU/GPU cluster, use of CUDA, MPI is a plus. Experience Experience with open-source
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and train CNN and SNN models utilizing frameworks such as Keras, PyTorch, and SNNtorch Implement GPU acceleration through CUDA to enable efficient neural network training Apply hardware-aware design