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structures, Bayesian approaches are proposed along with the supersaturated and D-optimal designs in the literature. This project aims to explore the current literature on Bayesian supersaturated D-optimal
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at Stockholm University. We have a strong tradition in sampling but areas that we are growing in include, but are not limited to, Bayesian inference, the intersection of statistics and machine learning
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strengths in Bayesian and Monte Carlo Methods, Biomathematics, Biostatistics and Ecology, Computational Mathematics, Data Science, Dynamical Systems and Integrability, Finance and Risk Analysis, Mathematical
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research strengths in Bayesian and Monte Carlo Methods, Biostatistics and Ecology, Combinatorics, Data Science, Finance and Risk Analysis, Nonparametric Statistics, Optimisation, Stochastic Analysis, and
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’ or ‘internationally excellent’. The highly research active SP Section comprises 13 permanent academic staff with research interests in Bayesian computational statistics and machine learning, uncertainty quantification
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Interview Motivated in learning new methodologies and applying new knowledge Essential Interview Knowledge of the approximate Bayesian machine learning (e.g. MCMC) (assessed at: Application form/Interview
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Analysis, Optimization, Time Series Analysis, Survival Analysis, Actuarial mathematics, Data Mining and Bayesian Statistics are welcome. Candidates are also expected to teach post graduate student courses
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Research Associate to contribute to a project focused on robust Bayesian inference with possibility theory. Robust inference is crucial for many real applications in which datasets are invariably corrupted
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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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for a Postdoctoral Research Scientist position in applied mathematics and scientific computing, emphasizing inverse problems in seismology and Bayesian analysis. The position is associated with