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Field
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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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and domains. Ability to troubleshoot connectivity issues and familiarity with vulnerability management and patching processes. Python and nVidia CUDA modules setup and configuration. Relational database
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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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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
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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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CUDA. What we can offer you: An opportunity to play a key role in a multidisciplinary team driving innovation in the deployment of deep neural network (DNN) models across a continuum of edge-to-cloud
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-based systems). Familiarity with GPU-accelerated computing (e.g., CUDA, NVIDIA ecosystem). Preferred Qualifications Education: No additional education beyond what is stated in the Required Qualifications
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, successful experience with parallel programming using languages such as OpenCL and/or CUDA 7. Demonstrated, successful experience with source code version control systems such as Git, Subversion, or similar 8
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) environments 6. Demonstrated, successful experience with parallel programming using languages such as OpenCL and/or CUDA 7. Demonstrated, successful experience with source code version control systems such as