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entitled “Beyond Data-Augmentation: Advancing Bayesian Inference for Stochastic Disease Transmission Models”. The overarching aim of the project is to develop the next generation of statistical tools
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physics-based insights with data-driven methods—such as physics-informed neural networks, surrogate models and Bayesian optimisation—to explain formation behaviour, identify early indicators of cell
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The successful candidate will join a global network of Simons Postdoctoral Fellows in Strong Gravity and Black Holes that are part of the Simons-BHSG. This new, multidisciplinary and multinational collaboration
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Doctoral (PhD) Candidate to join the new MSCA Doctoral Network FairCFD (https://www.imft.fr/faircfd/project-presentation/ ). The candidate will enrol for a PhD in Chemical Engineering at the University
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The successful candidate will join a global network of Simons Postdoctoral Fellows in Strong Gravity and Black Holes that are part of the Simons-BHSG. This new, multidisciplinary and multinational collaboration
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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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mathematics, statistics, or machine learning, or a closely related discipline • OR near to completion of a PhD • Expert knowledge of Bayesian computation and deep learning methods • Excellent
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getting Bayesian type uncertainty for parameters given data (i.e., a posterior type distribution over the parameter space) without specifying a model nor a prior. Such methods can in principle be applied
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of their mental models into a machine learning model, using dynamic Bayesian networks to understand, propagate and reduce uncertainty in their assessments. The research will apply models of distributed situation
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, using dynamic Bayesian networks to understand, propagate and reduce uncertainty in their assessments. The research will apply models of distributed situation awareness and ecological interface design