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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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detection framework for tipping points. Contribute to the design of scalable and interpretable forecasting strategies for large climate simulators, integrating adaptive sampling and Bayesian techniques
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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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statistical shape analysis, Riemannian geometry, time series and stochastic processes, and Bayesian statistics. Key responsibilities: To carry out research within the framework of the project, under
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statistical shape analysis, Riemannian geometry, time series and stochastic processes, and Bayesian statistics. Key responsibilities: To carry out research within the framework of the project, under
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on large-scale analysis of complex traits, including Bayesian machine learning and linear mixed model approaches for trait prediction and association in high-dimensional genomic datasets, as well as methods
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work on large-scale analysis of complex traits, including Bayesian machine learning and linear mixed model approaches for trait prediction and association in high-dimensional genomic datasets, as
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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
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closely with data scientists to interpret and predict MFA data using nonlinear reaction-diffusion models, 13C-isotopomer analysis, and MATLAB-based simulations enhanced by Bayesian Machine Learning