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the Alps supercomputer at the Swiss National Supercomputing Centre (CSCS), which features over 10,000 NVIDIA Grace Hopper GPUs, making it one of the most powerful AI-focused computing resources in
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. Communicate software engineering concepts to project teams with varying levels of expertise. Serve as a liaison with Princeton Research Computing staff on GPU cluster-related issues. Application of Domain
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: Applications accepted all year round Details In this project you will combine state of the art software development approaches for GPU programming with the study of interactions of high-energy particles with
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interfaces (APIs), servers and other web resources open to the broader scientific community. Support the on-site HPC Linux GPU cluster with Slurm queuing system. Maintain RAID NFS storage, Lustre storage
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Expanse machine (a five-year ~$25-million project supported by the NSF and enables tens of thousands of users to use HPC, HTC and GPUs), Voyager machine (a five-year, ~$12-million project supported by
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computing systems, GPU programming, machine learning, and/or full-stack. Experience teaching best practices in software engineering. Ability to work with multi-disciplinary teams and to interact cross
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interfaces (APIs), servers and other web resources open to the broader scientific community. Support the on-site HPC Linux GPU cluster with Slurm queuing system. Maintain RAID NFS storage, Lustre storage
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as code” approach to systems automation. You’ll be working across a range of predominately Linux based systems, including HPC and GPU accelerated compute, large-scale and high-performance storage, and
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/GPU architectures and even quantum computing are currently actively explored. Candidates are sought that align with these research areas. A broad interest in teaching topics would additionally be
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) and parallel computing, with a focus on cost-efficient and scalable model deployment. Skilled in working with medium-large scale multicore and heterogeneous (CPU + GPU) clusters. Excellent verbal and