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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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precision algorithms for CPUs and GPUs. Performance engineering and analysis including application profiling, benchmarking to identify performance bottlenecks. Verification, and validation of the developed
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management, high-performance computing systems, GPU acceleration, and parallel file systems * Documented experience with container and cloud technologies such as Docker, Helm, and Kubernetes * Ability
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frameworks (e.g., PyTorch). Engineering skills: GPU/cluster training, experiment tracking, data engineering. Ability to formulate research questions, run empirical studies at scale. *for students with