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Field
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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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in GPU programming one or more parallel computing models, including SYCL, CUDA, HIP, or OpenMP Experience with scientific computing and software development on HPC systems Ability to conduct
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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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high-performance computing capabilities (including approximately 4,000 Nvidia RTX 4000 Ada GPUs and over 30,000 CPU cores) hosted at the project data center in Nevada where the telescope is located
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structured and unstructured datasets, and GPU-accelerated computing. Proven experience with Large Language Models. Required Skill/Ability 3: Sound background in theoretical and applied machine learning/deep
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for performance, cost-efficiency, and low-latency inference Develop distributed model training and inference architectures leveraging GPU-based compute resources Implement server-less and containerized solutions
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computational infrastructure such as A100 and H100 GPUs, combined with pre-processed large-scale biobank data such as UK Biobank and ADSP, enabling you to work at the scale required for breakthrough research
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. Experience with algorithm design, embedded DSP development, multithreaded programming, GPU development, SDR hardware platforms, FPGA development, and/or Linux-based designed tools is desired. Representative