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Field
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paradigms, and distributed and parallel programming constructs. Minimum qualifications: A master’s degree or equivalent professional experience in Computer Science, Computer Engineering, or related fields Per
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computers (e.g., parallelizing and distributing code). Experience in distributed data management and workflow systems. Preferred Competencies Ability to build systems where agents operate with independence
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University of California, San Francisco | San Francisco, California | United States | about 8 hours ago
on of effective approaches. This specialist may be required to utilize the elasticity of the AWS Cloud for Big Data Intensive (e.g. Hadoop/Spark) compute infrastructure and parallel system environment
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, identifies, evaluates, enables, and promotes existing and emerging digital media, surveillance, and instructional technologies and their application. Design, install, program, configure and maintain new
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management, health and safety issues, access approval, and campus security. Oversee distribution, expenditures and payments (approval) of departmental Procurement or Travel Cards. Ensure adherence to all
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management, cache optimization, and vectorization techniques. Strong understanding of algorithms and data structures, especially those suitable for parallel processing and distributed computing. Understanding
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, appointment with tenure is possible. We particularly encourage applications from candidates whose research focuses on Computer Systems (including real-time and embedded systems, distributed systems, networking
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programming; Experience programming distributed systems; Experience with parallel and distributed File Systems (e.g., Lustre, GPFS, Ceph) development. Advanced experience with high-performance computing and/or
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Research Scientific Computing Center (NERSC) is inviting applications for the position of Storage Systems Group (SSG) Lead. NERSC's mission is to accelerate scientific discovery through high performance
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for Science @ Scale: Pretraining, instruction tuning, continued pretraining, Mixture-of-Experts; distributed training/inference (FSDP, DeepSpeed, Megatron-LM, tensor/sequence parallelism); scalable evaluation