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Elhoseiny, Code: https://github.com/yli1/CLCL Uncertainty-guided Continual Learning with Bayesian Neural Networks (ICLR’20), Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, Marcus Rohrbach, Code: https
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tasks require high-frequency evaluations of forward models, in order to quantify the uncertainties of rock and fluid properties in the subsurface formations. Therefore, the objectives of this research
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the center's strategic objectives. Develop advanced products, technologies, and innovative methodologies, translating research findings into tangible solutions with potential for commercialization and societal
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, Uncertainty quantification, Approximation Theory, Applied Probability and Bayesian statistics, Optimal Control and Dynamic Programming. Appointment, salary, and benefits. The appointment period is two years
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effective solution to mitigate GHG emissions that can be deployed at large scales. CCS may enable the industry to continue operating with reduced environmental footprint. The objective of this project is to
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integrate complex flow on Discrete Fracture Networks (DFN). The objective of this project is to develop a tool to generate DFN models amenable for multiphase flow, and scale up the model to be usable with