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physics, Bayesian inference, and complex systems theory. You will contribute to method development, simulation and validation in close collaboration with experimental partners. Key Responsibilities: Carry
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startup funding and starting salaries are anticipated. The department encourages people from all areas of research to apply and is particularly interested in expertise in the broad areas of Bayesian
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of statistical analyses, in particular: Exploratory and confirmatory factor analysis, Multilevel analyses (including latent class analysis), Time series analysis, Bayesian inference methods, Regression techniques
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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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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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-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
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of biofouling processes in marine environments. This role will focus on developing and applying Bayesian statistical models to investigate and predict biofouling patterns to enhance our understanding of how