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working with climate, weather and earth datasets formats (netcdf, zarr,..) . Working knowledge of High-performance computing (HPC). Experience with GPU-accelerated machine learning frameworks such as RAPIDS
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. Experience with GPUs and performance optimization of ML at scale. Familiarity with containerization and HPC workflows. Notes: This is a full-time, career appointment, exempt (monthly paid) from overtime pay
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likely to be required include advanced Python or c++ with experience in developing and fine-tuning foundation models for modelling tasks, including use of HPC systems and multi-GPU programming. Knowledge
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research computing and visualization services. ARC systems currently host 50,000+ CPU cores, 500+ advanced GPUs, and 10+ petabytes of storage. We stay abreast of novel and developing trends in research
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 16 hours ago
Engineer will support the development and operation of the GPU-accelerated, real-time data analysis pipelines that will turn images from the world’s largest digital camera into discoveries. Three years
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service deployments, or publications in AI/science venues. Experience with GPUs and performance optimization of ML at scale. Familiarity with containerization and HPC workflows. For full consideration
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, including GPU systems capable of running large-scale AI workflows. Applicants should submit online at https://www.princeton.edu/acad-positions/position/39681 and include a cover letter, curriculum vitae
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, combining scientific excellence with real-world impact. They operate within a unique ecosystem that includes the AI Foundry (state-of-the-art GPUs and engineering capacity), the System for User Knowledge (SUK
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, the college recently established a new high-end GPU-based high-performance computing system designed specifically for AI applications. The Department of Mechanical Engineering (MECH) at CSU offers a curriculum
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Facility computing infrastructure supports the diverse needs of over 2,500 researchers in the six UC Davis colleges and schools, totaling some 48,000 CPUs, over 200 GPU nodes, and dozens of petabytes in user