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Field
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-year research programme, funded by NWO(link is external) , EMBRACER brings together a wide range of world-leading climate experts with the aim to address existing uncertainties about climate
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and data analytics (including machine learning and deep learning); from high-performance computing to high-performance analytics; from data integration to data-related topics such as uncertainty
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potential to: mitigate redistribution of actions, due to long-term loading and moisture changes over time reduce uncertainties of actions due to material/connection stiffness variability for high seismic
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integrates machine learning and statistics to improve the efficiency and scalability of statistical algorithms. The project will develop innovative techniques to accelerate computational methods in uncertainty
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existing uncertainties about climate feedbacks at the boundaries between oceans, land, ice, and atmosphere. Our interdisciplinary approach and state-of-the-art infrastructure will bring us forward in our
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change, Earth’s climate system and climate feedbacks. The programme brings together a wide range of world-leading climate experts with the aim to address existing uncertainties about climate feedbacks
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to investigate the cognitive and social behaviour (such as knowledge acquisition, organization, and transmission, recognition processes, or risk assessment and decision-making under uncertainty) involved in
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of investigation, many predictive tools lack robust ways to incorporate uncertainties in boundary conditions, turbulence modelling, and manufacturing variability. Problem Statement Conventional CFD workflows assume
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providing a basis for decision support and lifetime extension. This may be obtained by comparing existing design practice with results based on application of Bayesian updating to account for uncertainties in
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on developing machine learning algorithms to support the use of complex urban simulators in decision-making under uncertainty. This PhD project shifts the focus from optimality to relevance in urban land-use and