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learning, multicore and GPU programming, and highly parallel systems. Good knowledge in one or more of the following programming languages/environments: C/C++, Python, PyTorch (or similar), and Cuda. Place
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Proficiency in CUDA programming and hardware-accelerated computing Experience with high-performance I/O and efficient data access protocols for distributed scientific computing Expertise in customizing data
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and distributed systems, for example using programming models like OpenMP, CUDA, SYCL, and MPI. Experience with distributed software development workflows (e.g., Git, code review, unit testing, CI/CD
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