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Field
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. Familiarity with data formats common in scientific domains such as medical imaging, genomic sequences, proteins, chemical structures, geospatial, oceanographic, and heath record data. Experience in CUDA GPU
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, VMware. Experience building and running containerized applications in an HPC environment. Knowledge of Apptainer, Warewulf, Fuzzball. Experience managing systems using GPU/CUDA clusters for AI/ML and/or
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support. Deep understanding of modern machine learning architectures and optimization techniques. Proficiency in deep learning frameworks (PyTorch). Experience with Nvidia GPU stack and HPC technologies
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dynamic research environments, exhaustive training opportunities and institutional collaborations. The PhD candidate will benefit from the computational resources available at CEPAM (GPU servers). He/She
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-focused GPUs. Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Promote equal opportunity by fostering a
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training aids, test equipment, shop equipment, and hand tools Perform routine maintenance on fixed and rotary wing aircraft Operate and maintain ground support equipment (e.g., tugs, forklifts, GPUs, snow
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. Operates and maintains airport ground support equipment, such as aircraft tugs, ground power unit (GPU), lavatory service cart, de-icing cart, forklift, golf cart, UTV, and airport shuttles and vehicles
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containerisation (e.g., Docker) and orchestration tools (e.g., Kubernetes) for deploying and managing applications at scale, including support for GPU-accelerated applications. Data Engineering Tools: Proficiency in
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-unterstütze Simulation« team offers you exactly that. What you will do Optimizing existing code for electronics application considering multi-CPU and multi-GPU usage (implementation in jax and/or numpy and/or C
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skilled HPC System Administrator to join its Systems and Operations Team. This position will support the deployment, maintenance, and automation of RCC's HPC systems, including CPU/GPU clusters, storage