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University of British Columbia | Northern British Columbia Fort Nelson, British Columbia | Canada | about 2 months ago
on developing mathematical models and control theory tools to support sustainable fisheries management. The project will optimize fishing practices to reduce ecological impacts, providing actionable insights
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engineering or equivalent field and 0-2 years of research experience. Background in quantitative analysis, mathematical modeling, data science and simulation techniques with expertise in optimization techniques
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-driven discovery of new physics experiments to test quantum-gravity and observe gravitational waves (examples here and here ) Inventing state-of-the-art AI-driven exploration, optimization, and search
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design, optimization, and sizing using phenomenological and/or CFD methods. Energy system analysis. Strong analytical and problem-solving skills, with an ability to interpret and analyze simulation and/or
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advancements and practical implementations optimized for modern HPC systems. The postdoc will primarily contribute to one or more of the following research areas: Development of efficient numerical linear
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will be tailored to your expertise, spanning from hardware design to system-level optimization and control methods. For the AI position, you will develop machine learning models that incorporate physical
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Sriperumbudur. Potential research projects include (but are not limited to) developing theory and methods for metric-valued (including functions, distributions) data analysis, optimal transport and gradient flows
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journals and conferences. This role provides a unique opportunity to work with the world’s first exascale system, Frontier, and collaborate with leading experts in machine learning, optimization, electric
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base, the partnership will bring together the University of Oxford’s expertise in statistics, mathematics, engineering and AI with industry scientists. Within the partnership, small research teams will
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been shown to accelerate and improve the training procedure of SNNs by defining new cost functions that are differentiable and easier to optimize. They can also handle quantized weights, e.g., using