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Field
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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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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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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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systems. Areas of interest may include, but are not limited to: - Advanced accelerator chip technologies, such as GPUs or other specialized chips for large-scale AI processing - High-speed memory
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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
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dedicated to research, advanced medical data streaming and processing machines, as well as state-of-the-art local and scalable cloud-based, compute infrastructure (CPU, GPU). Long-standing and very successful
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. This position offers substantial resources, including access to the SeaWulf computing cluster and cutting-edge GPU clusters housed at IACS and CEWIT. The Empire Innovation Professor will join a robust community
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quantitative genetics, machine learning, bioinformatics, and population genetics, and their applications in an agricultural setting A modern dedicated computational infrastructure (CPUs & GPUs) Well-developed in
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facility will combine traditional compute provision with AI focused GPU capabilities. What you’ll do Work with colleagues from the University to Lead efforts for the Secure Data Research Environments IT
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facility will combine traditional compute provision with AI focused GPU capabilities. What you’ll do Work with colleagues from the University to deliver the Secure Data Research Environments IT