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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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Experience with Pytorch, MONAI, CUDA or equivalent software libraries for developing deep learning models. Familiarity with medical image such as MRI, CT, or volumetric ultrasound. Knowledge on common medical
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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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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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(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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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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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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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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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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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