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Field
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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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documenting their work as well as travel to and present at those events where appropriate. Current projects include: parallel numerical optimization with a focus on GPU acceleration, efficient embedded edge
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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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turbulence. Experience with GPU programming, FPGA, and DNN in image recognition is a great plus. Track record of publications and conference presentations. Experience with hands on lab work. FLSA Exempt Full
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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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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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) 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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. 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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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