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Field
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. Experience working in high-performance computing (HPC), distributed compute, or accelerated environments (GPUs, multi-node systems). Solid background in database systems, including: Relational databases (e.g
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implement ensemble learning algorithms and optimization strategies for large-scale or streaming data. Develop parallelized and GPU-accelerated learning modules, ensuring scalability and performance efficiency
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computational resources. At IP2I, dedicated scientific computing infrastructures, including CPU and GPU clusters, HPC facilities, and big-data environments, are available for large-scale simulations and data
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one or more of the following areas: Programming, Algorithms and computational complexity, Programming languages and compilers, Computer graphics, and Parallel and GPU computing. A typical teaching
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. The Interpretable Machine Learning Lab has dedicated access to high-performance CPU and GPU computing resources provided by Duke University’s Research Computing unit and state-of-the-art IT infrastructure. Ideal
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lead in designing, deploying, enhancing, and managing our HPC infrastructure. This infrastructure includes GPU clusters (B200/H200/A40), liquid-cooled CPU cluster, and a cloud-based HPC system, with
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, including a GPU cluster, and strong international networks. Terms of employment The average weekly working hours are 37 hours per week. The position is a fixed-term position limited to a period of three years
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University of North Carolina Wilmington | Wilmington, North Carolina | United States | about 2 months ago
. (Experience may include time while doing graduate studies.) Experience with GPU programming for scientific/engineering computations. Experience using containerization software (such as docker or apptainer) Two
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Interest & knowledge about oncology & immunology, molecular & cellular mechanisms Work with ML + generative AI + GPUs at scale Experience with clinical or biomedical data and workflows Enjoy multimodal
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that combine parallel architectures (i.e., GPUs or accelerator boards, clusters) and numerical algorithms suited to such architectures with the goal of improving the speed of convergence and the stability