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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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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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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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like LiteBIRD and FOSSIL. We have a large international network of collaborators, and as a doctoral fellow you will have many opportunities to travel and collaborate with researchers at other
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radio interferometry data, particularly very long baseline interferometry. Experience with or skills relevant to statistical modelling and Bayesian inference. Demonstrated familiarity with the fields of X
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Influenza Research and Response (CEIRR) as part of the NIH NIAID CEIRR collaborative network (https://www.ceirr-network.org/centers/penn-ceirr ). The participant will have the opportunity to attend local and
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leads in the area of electrode materials characterisation for direct ammonia solid oxide fuel cells Attend progress and management meetings as required and network with the other research groups where