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Intensive Care National Audit & Research Centre - ICNARC; | London, England | United Kingdom | 4 days ago
Clinical Trials Unit (CTU) that is designing and delivering cutting-edge clinical trials employing frequentist and Bayesian methods in adaptive and platform trial designs. We are looking for a Principal
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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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Bayesian ML approaches for path inference; introducing sensors; behaviour classification; resource-constrained active-learning; other IoT applications; microbattery development and field experiments and
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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 parametrization of these models based on least squares and Bayesian calibration techniques employing longitudinal series of anonymized PSA data from patients. 3) Analysis of the predictions, parameters, and
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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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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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). -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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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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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