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, applications, tools, and services for the broader research community to use data at scale to pursue scientific inquiry and accelerate discovery. Learn more at https://gdc.cancer.gov/, https://gen3.org/, https
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, applications, tools, and services for the broader research community to use data at scale to pursue scientific inquiry and accelerate discovery. Learn more at https://gdc.cancer.gov/, https://gen3.org/, https
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description L'objectif de cette thèse est donc de concevoir un environnement de
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to enable massively parallel processing of ABMs on NVIDIA graphics processing units (GPUs), without the need for specialist understanding of GPU programming or optimisation. This project will explore
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supportive team environment that promotes collaboration and knowledge sharing. Access to world-class computational infrastructure, GPU-based computing environments, and unique high-quality cryoET datasets
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empowers researchers to solve complex problems through a massive ecosystem of 50,000+ CPU cores, 1,000+ GPU resources, and 50PB+ of storage. At CHPC, we foster a positive, collaborative environment where
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aspect of the comprehensive rewards package. The Cardinal at Work website (https://cardinalatwork.stanford.edu/benefits-rewards ) provides detailed information on Stanford's extensive range of benefits and
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(UTC) Country Sweden Type of Contract To be defined Job Status Full-time Hours Per Week To be defined Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the
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ALMA MATER STUDIORUM - UNIVERSITA' DI BOLOGNA - - DIPARTIMENTO DI INGEGNERIA INDUSTRIALE | Italy | about 1 month ago
Description Analysis and development of methodologies to accelerate the computation of numerical optimization through parallelization and the use of GPUs. Where to apply Website http://www.unibo.it Requirements
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international journals or conferences. Experience in predictive modeling for forecasting or recommendation systems. Strong programming skills in Python and AI frameworks (PyTorch, TensorFlow), including GPU/cloud