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& GPUs — and deep integration with clinical data including electronic health records A diverse array of ongoing research, education, patient care, and community centered activities that require increasing
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free text of both biomedical literature and electronic patient records exploiting HPC, including GPUs embedded within NHS infrastructure. Development and deployment of ML operations software and tooling
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | 3 months ago
. The researcher(s) will be provided access to state-of-the-art supercomputing facilities with advanced GPU and data storage capabilities. Additionally, opportunities will be available for collaborations. Duties
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systems and GPU-accelerated processing. Proficient in computing languages for software/hardware integration, data analysis, networking; specifically, Python and C/C++. Proficient with Linux computer systems
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showcases and visualisations; and scaling and optimising data science and AI research to leverage HPC and GPU infrastructure. This post is full time and open ended (permanent) Apply online at: Research
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optimizing PIC algorithms for modern heterogeneous architectures, including CPUs, GPUs, and other accelerators, the project seeks to achieve unprecedented efficiency and resolution in plasma simulations
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. Prior work with databases used for organizing large-scale processing. Prior use of and/or software development with GPUs. Experience with visualization of large data sets. An understanding of how to make
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-performance computing facility managed by the University with a general-purpose cluster consisting of 16,616 CPU cores and approximately 100 GPUs, as well as a dedicated AI cluster with 40 A100/H100 GPUs
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performing routine maintenance on lab computing resources (e.g., Linux servers, GPUs, networked workstations). (10%) Performs other tasks, duties, and responsibilities as required Minimum Qualifications
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The recruited engineer will be co-supervised by MdS and Joliot to implement this new approach. To achieve a portable, efficient code that fully exploits GPUs, the core of the code will be developed