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plasma-material interactions in fusion energy systems. You will also advance knowledge of key AI methods such as deep learning, operator learning, and Bayesian optimization, and apply it to develop next
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courses within the research methodology (Data Collection) and statistics modules (Mixed Models, Bayesian Approaches, Network Models, etc.). Additionally, the candidate may also contribute to teaching across
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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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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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). -Interest in Bayesian inference. - Knowledge of non-Gaussian models (heavy-tailed, impulsive) is an asset. Additional Information Work Location(s) Number of offers available1Company/InstituteUniversité
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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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leadership in the statistical sciences, at the local, national and international levels. Rice Statistics has strengths in Bayesian statistics, machine learning, multivariate analysis, dependent data, data
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-armed Bandits, Bayesian Optimization. Automated Model Design and Tuning: Neural Architecture Search, Hyperparameter Optimization. Computer Networking: Resource-Constrained Networking (e.g., Internet
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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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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