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. Training LLMs, large-scale deep learning systems, and/or large foundation models using GPU/TPU parallelization while setting up the environment/system network under various constraints, such as limited
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 months ago
GPU accelerated pipelines. In collaboration with a worldwide network of real-time data release and processing centers, the Data Access Engineer will take the alert distribution system to production
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package, including health and life insurance, generous paid leave and retirement programs. To learn more about USC benefits, access the "Working at USC" section on the Applicant Portal at https
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support the IIT High Performance Computing systems. The candidate will be the main system administrator of four HPC clusters with a total of about 360 GPUs. Within the team, your main responsibilities will
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insurance, generous paid leave and retirement programs. To learn more about USC benefits, access the "Working at USC" section on the Applicant Portal at https://uscjobs.sc.edu. Position Description Advertised
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The Computer Science program in the Computer, Electrical and Mathematical Sciences and Engineering Division (https://cemse.kaust.edu.sa ) at King Abdullah University of Science and Technology (KAUST
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Assistant Research Professor (Research Track), Radiology and Imaging Sciences Posting Number req24876 Department Radiology & Imaging Sci Dept Department Website Link https://radiology.medicine.arizona.edu
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, immersive, and interactive technologies. Highly proficient in real-time engines, AI-assisted tools, GPU-accelerated platforms, and emerging computational design workflows, you combine creativity with
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research profile, and an international network around big data in marine sciences. The candidate will have access to NIOZ’s high-performance computing cluster, GPU nodes for deep learning, dedicated data
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that serves researchers and educators at the University of Utah and beyond. Responsibilities Kubernetes for AI/ML: Design and deploy highly available Kubernetes clusters, optimized for GPU utilization and AI/ML