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Field
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Location: South Kensington Campus About the role: We are looking for a motivated Research Associate in Bayesian Optimisation & Experimental Design to work with Professor Ruth Misener and Dr Calvin
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The relationship between the information-theoretic Bayesian minimum message length (MML) principle and the notion of Solomonoff-Kolmogorov complexity from algorithmic information theory (Wallace and
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increasingly important, but also more complex, due to rising demands on performance, precision, quality, and sustainability. Bayesian optimization (BO) - a special machine learning approach - represents a
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plants they visit and pollinate. Bayesian networks (BNs), and other probabilistic graphical models, can provide a visual representation of the underlying structure of a complex system by representing
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This PhD project is funded by a successful ARC Discovery Project grant: "Improving human reasoning with causal Bayesian networks: a user-centric, multimodal, interactive approach" and the successful
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will focus on developing and applying Bayesian statistical models to investigate and predict biofouling patterns to enhance our understanding of how environmental factors and antifouling technologies
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Scalable Inference: Develop new algorithms for scalable uncertainty quantification (UQ) and Bayesian inference and apply them to challenging simulation problems. The goal is to produce robust, validated
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-based simulation models · Knowledge of Bayesian and Markovian calibration methods · Expertise in evaluating agent-based model outputs · Experience in “debugging” agent-based simulation
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including, but not limited to: Bayesian statistics, computational statistics, inverse problems, numerical analysis, probability, statistical machine learning, stochastic analysis, and uncertainty
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Probability, Regression Analysis, Multivariate Analysis, Categorical Data Analysis, Optimization, Time Series Analysis, Survival Analysis, Actuarial mathematics, Data Mining and Bayesian Statistics are welcome