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, or approximate computing. Knowledge of hardware security, fault tolerance, dependable computing, or hardware reliability. Experience with GPU, FPGA, embedded, or specialized AI accelerator platforms. Experience
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, GPU) and workshops for mechanical, electrical and electronic development projects. Long-standing and very successful cooperations with industry and clinical partners (cardiology, radiology) offer
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research-focused HPC environment. Project background We train open foundation models with hundreds of billions of parameters on thousands of GPUs on one of the largest AI-ready supercomputers in Europe
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Data (CRCD), which operates a large multi-thousand-core cluster with GPU co-processors and PhD-level consulting staff. Pitt ECE also leads the NSF Center for Space, High-performance, and Resilient
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Data (CRCD), which operates a large multi-thousand-core cluster with GPU co-processors and PhD-level consulting staff. Pitt ECE also leads the NSF Center for Space, High-performance, and Resilient
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/SCI Clearance Master’s degree Training and optimizing ML algorithms on GPU hardware architectures, specifically NVIDIA based Working with geo-spatial data Statistics, multivariable calculus, and linear
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of CPU and GPU resources to maximize model performance, scalability, and portability. A particular focus of the position will be a dedicated project to prepare, optimize, and benchmark the code
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innovative software solutions to climate modeling problems that represent the state-of-the-art in high-performance computing and are performant on GPUs. Contribute to the design, performance specification, and
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. The Directorate comprises the International Office, led by the Deputy Director International; the Global Partnerships Unit (GPU), led by the Associate Director Transnational Education (TNE); DMU Global; and the
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Position Details Position Information Recruitment/Posting Title Lead Software Developer – GPU-accelerated Free Energy Simulation and Machine Learning Methods Department Quantitative Biomedicine Inst