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techniques from statistical physics, Bayesian inference, and complex systems theory to address challenges posed by noisy and incomplete data. Depending on the results obtained in the first year, the post can
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related to gravitational wave astronomy. The primary aim will be the development of advanced approaches for computational Bayesian Inference to measure the properties of Compact Binary Coalescence signals
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). The position is funded by NSF-EPSRC grant 'Stochastic Shape Processes and Inference', in collaboration with the University of Nottingham, Ohio State University, and Florida State University. The successful
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). The position is funded by NSF-EPSRC grant ‘Stochastic Shape Processes and Inference’, in collaboration with the University of Nottingham, Ohio State University, and Florida State University. The successful
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methods for complex trait analysis, scalable Bayesian and deep learning approaches, or algorithms for inferring and analysing large-scale graph data structures. Experience in statistical and population
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involve developing methods for complex trait analysis, scalable Bayesian and deep learning approaches, or algorithms for inferring and analysing large-scale graph data structures. Experience in statistical
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(e.g., maximum parsimony; Bayesian inference), and utilizing these phylogenies to quantify evolutionary rates, directionality of trends, and patterns of morphological space occupation. You will have (or
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and PhD students. Research spans a wide range. Current interests include: Bayesian statistics; modelling of structure, geometry, and shape; statistical machine learning; computational statistics; high