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/malariasimulation ) to link geospatial predictions of insecticide resistance to malaria disease incidence. Resulting models and maps will identify areas where targeted deployment of novel insecticidal interventions
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projects, including: The post-holder will run numerical models that simulate the dispersion of greenhouse gases through the atmosphere. These models will be used, in Bayesian inference frameworks
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international projects, including: The post-holder will run numerical models that simulate the dispersion of greenhouse gases through the atmosphere. These models will be used, in Bayesian inference frameworks
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strategies. Your tasks in detail: Enhance existing Bayesian state estimation with reliability margins using both simulated and, if necessary, real-world grid data. Develop Use-Case-Specific Reinforcement
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project by addressing specific case studies or specific targeted techniques. The main tools to be used will come from the discipline of Machine Learning, particularly those based on Bayesian methods
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and reduction Development and application of big data analytics for large X-ray data sets Application of Bayesian methods to X-ray data Combinatorial analysis of various data from complementary
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main project by addressing specific case studies or specific targeted techniques. The main tools to be used will come from the discipline of Machine Learning, particularly those based on Bayesian methods
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campaigns including programmed screening or Bayesian optimisation. You will characterise the resulting materials, in terms of their properties and performance for an intended application. Sustainability will
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, kernel machines, decision trees and forests, neural networks, boosting and model aggregation, Bayesian inference and model selection, and variational inference. Practical and theoretical understanding
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phylogenetic analysis over traditional Bayesian methods, and this capacity for improvements will have substantially more impact on the more complex MSC model. The project will develop an efficient framework