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department by carrying out both quantum information and computation projects ranging from quantum device characterization, error mitigation/suppression/correction, Bayesian-inference-based quantum information
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anomaly detection Bayesian methods and uncertainty quantification Graph Neural Networks (GNNs) Spatiotemporal data engineering Digital twins and simulation Demonstrated Applied AI for Healthcare and
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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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to complementary expertise, scientific activities, and national research networks. The purpose of the fellowships is research training leading to the successful completion of a PhD degree. Successful applicants will
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interdisciplinary research environment, offering all recruited candidates access to complementary expertise, scientific activities, and national research networks. The purpose of the fellowships is research training