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, cross-spectra, filtering, mode fitting). Inverse problems / inference applied to astrophysical flows (e.g., inversion methods, Bayesian/statistical inference, uncertainty quantification) Strong
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for learning about models from data, 2) incorporation of expert knowledge in model building through Bayesian prior elicitation, and 3) develop new methods for identification of conflicts in different parts
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quantification, in particular the theory and methods known as predictive Bayes. Predictive Bayes theory involves getting Bayesian type uncertainty for parameters given data (i.e., a posterior type distribution
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to astrophysical flows (e.g., inversion methods, Bayesian/statistical inference, uncertainty quantification) Strong programming and data-analysis competence; ability to produce reproducible workflows. Experience
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getting Bayesian type uncertainty for parameters given data (i.e., a posterior type distribution over the parameter space) without specifying a model nor a prior. Such methods can in principle be applied