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computational modeling, geometric morphometrics, multivariate and Bayesian statistics, spatiotemporal and spatial modeling (including GIS), causal inference, machine learning, AI, and statistical software
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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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structured around two main pillars: Network resilience and sovereignty, i.e., research on networking architectures and mechanisms that keep critical networks and applications they support running optimally
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. Experience in the implementation of mathematical or statistical models and model fitting, including Bayesian model fitting, is desirable but not essential. Familiarity or experience of management and analysis
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, mathematical psychology (computational modelling) and/or human factors methods and related statistical techniques (including Bayesian hierarchical methods) Experience with the development and application
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in a connected context, investigating how accelerators can be designed, integrated, and scaled for emerging large scale and networked applications. We are interested in new ways of virtualising
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, data mining, Bayesian methods, and statistical learning About Working at the Crick Our values We are bold. We make space for creative, dynamic and imaginative ideas and approaches. We’re not afraid to do
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modelling of climate-sensitive infectious diseases, with a particular emphasis on Bayesian hierarchical modeling using Integrated Nested Laplace Approximation (INLA). The work will contribute to ongoing
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more novel problems. Keywords include: automatic experimental design, Bayesian inference, human-in-the-loop learning, machine teaching, privacy-preserving learning, reinforcement learning, inverse
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or a numerate discipline OR equivalent experience. Broad knowledge of probabilistic models, Bayesian inference and machine learning methods. Good knowledge of R, Python or both (links to project source