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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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research related software, Python, R, Matlab, Mathworks, Julia, Ansys, Intel, nVidia cuda and GCC compilers. Experience with dev ops tools such as GitHub, GitLab, Ansible, package management tools for rpm
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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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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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languages used: C++ 2014, CUDA, Lua Desirable: - interest in high-performance computing with graphics processors (GPUs) and simulation methods - fluent knowledge of modern C++ and a scripting language
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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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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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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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MPI, OpenMP, CUDA or OpenACC. Familiarity with scientific software stacks or domain-specific tools (e.g., BWA, Samtools, GATK, Gromacs). Experience supporting research involving regulated data (e.g