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Inria, the French national research institute for the digital sciences | Rennes, Bretagne | France | 2 months ago
studies in developing flow models that incorporate noise to account for modelling uncertainties or errors. The introduction of noise into ocean dynamics models must be done on a theoretically rigorous
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development. The project addresses how advanced digital infrastructure can be used to reduce uncertainty, risk and variation in industrial product realisation. It connects engineering design, AI, digital twins
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through a mixture-of-experts architecture that adaptively combines specialized models according to pandemic phase and intervention regime. The framework aims to provide robust, uncertainty-aware forecasts
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StudyServiceCenter and four Departments (see https://univie.ac.at/psychologie) . The courses offered in Psychology includes a bachelor's, master's and doctoral studies. Currently there are nearly 4000 students
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project examines how instructional and assessment designs can support students’ epistemic thinking with respect to how they compare evidence, calibrate uncertainty, justify claims, and move between
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waters. The proposed work can address any range of spatial and temporal scales, from microbial to global, but must cover questions related to reducing uncertainties in warming feedback on methane, provide
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Researcher will work in Professor Benjamin Peherstorfer’s group (https://cims.nyu.edu/~pehersto/ ) on scientific machine learning at the Courant Institute of Mathematical Sciences where they will help
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under uncertainty. The role will suit a researcher with strong technical capability in quantum computing, data-driven modelling, infrastructure systems, computational optimisation, simulation
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The SDU Adaptive Intelligence Lab (ADIN Lab) (https://adinlab.github.io/ ) located under the Data Science and Statistics Section of the Department of Mathematics and Computer Science (IMADA
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Dresden, Sachsen | Germany | about 2 months ago
. Data-driven methods, for instance for inverse parameter identification or uncertainty quantification in fracture mechanics datasets, are not an end in themselves, but a compelling extension where they