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, CUDA) and good understanding of hardware used in large scale HPC clusters such as hybrid CPU+GPU systems, memory hierarchies and file systems; experience with job schedulers (e.g., Slurm, FLUX) 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 using SLURM or LSF Experience with PyTorch, JAX, or Tensorflow Experience with NVIDIA CUDA and related OpenMP programming Experience with cloud services (AWS, GCP, Azure, etc) Experience
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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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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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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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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