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Field
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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
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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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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
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., DeepSpeed, FSDP, Ray, or MPI-based systems). Familiarity with GPU-accelerated computing (e.g., CUDA, NVIDIA ecosystem). Preferred Qualifications Education: No additional education beyond what is stated in
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one of the above fields Very good expertise in the programming languages Python and C/C++, the numba library, and in applying parallelization techniques using GPU programming (CUDA/OpenCL) and MPI