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Field
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and comprehensive grasp of how GPU architecture affects performance for datacenter applications, especially Large Language Models (LLMs) Drive the discovery of opportunities for innovation in GPU
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sensitivity and wildfire plumes. You will work in a tandem with a fellow PhD student in computer science at Leiden University, who focuses on GPU optimization, while you lead the physics. Together you
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designing and operating massive-scale GPU and combined CPU/GPU workloads across these services. You will design and debug platforms, and will work closely with researchers as you co-design solutions
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on GPU/AI compute systems in a lab/workshop teaching environment. They will be experienced in: Python, PyTorch, TensorFlow and Hugging Face GPU and AI compute systems (NVIDIA GPUs, CUDA, cuDNN
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experience designing & operating massive-scale GPU and combined CPU/GPU workloads across these services. You will design and debug platforms, and will work closely with researchers as you co-design
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, Elastic Stack, or similar monitoring platforms. Experience supporting GPU computing environments, including NVIDIA drivers, CUDA , GPU scheduling, monitoring, and performance optimization; familiarity
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compute (CPU/GPU), high-speed interconnects, storage and parallel file systems, cluster management, and user-facing platform services. In practice, the Senior HPC Architect drives end-to-end
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picture of a national research platform: a multi-cluster Kubernetes estate (from a GPU-dense public cluster to a geo-distributed HA setup across three Brno sites), petabytes of fast storage, and the
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computational experiments. You will experience designing and operating massive-scale GPU and combined CPU/GPU workloads across these services. You will design and debug platforms, and will work closely
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high temperature. ● Experience extracting transport properties from molecular dynamics trajectories. ● Experience with GPU-accelerated machine learning frameworks (for example CUDA