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exposing hardware accelerators, such as GPUs and FPGAs, in a seamless and portable way. This includes designing execution logic and resource-scheduling strategies that make efficient use of available
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TensorFlow, PyTorch, etc.) Experience with GPU computing, especially for AI/ML calculations Understanding of multi-user computing systems, environments and networks Experience with teaching and/or tutoring in
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and Memorial Sloan-Kettering Cancer Center, NY. Read more about the project here: https://health.medarbejdere.au.dk/en/display/artikel/supercomputer-and-ai-to-strengthen-danish-cancer-treatment-new
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deformation, wave propagation, etc. Familiarity with containers, numeric libraries, modular software design. Experience doing performance analysis and tuning. Excellent C/C++ and Python programming skills. GPU
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transformations at such interfaces, and how they are influenced by external electric fields and electrolyte composition. Access to high performance computing facilities including GPU clusters will be provided
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that outperforms highly optimized code written by expert programmers and can target different hardware architectures (multicore, GPUs, FPGAs, and distributed machines). In order to have the best performance
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Finnish National Computing Center (LUMI, Puhti, and Mahti) with thousands of GPUs (A100 and V100) to use (research on large models is available). Help you to produce high-quality research outcomes with
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nearby highways, an indoor SDR testbed for research on next generation wireless communications and sensing, and a GPU Lab for training of advanced machine learning models. IDLab is both part of
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expertise in key machine & deep learning frameworks and toolsets. Experience in GPU computing, HPC, Containers & Image processing tools would be appreciated. A strong track record of publications in high
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systems, they increasingly reach their thermal limits due to rapidly rising power densities in modern CPUs and GPUs. Liquid cooling technologies, such as Direct-to-Chip (D2C) can dissipate higher heat loads