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model fitting, including Bayesian model fitting, is desirable but not essential. Familiarity or experience of management and analysis of large multidimensional real world data sets using Stata, R, Python
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the application of rock physics models, Bayesian inversion methods, and machine learning algorithms in the electromagnetic context. Qualifications and personal qualities: Applicants must hold a master’s degree (or
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of identifying excellent researchers and accelerating them in using AI to advance and disrupt Science or Engineering. Here ‘AI’ is interpreted very broadly, e.g.: topics in Bayesian Inference and Robotics
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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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, 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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, 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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. 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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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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interpreted very broadly, e.g.: topics in Bayesian Inference and Robotics; ‘Science’ covers any typical topic in Natural Science and Engineering (Epidemiology, Biology and basic science in biomedicine
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, they will have prior knowledge of infectious disease modelling, Bayesian inference methods and optimisation methods. They will have a developing research profile, with a demonstrated ability to publish