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datasets spanning electrochemical testing, embedded sensors, environmental logging, spectroscopy and advanced imaging. They will create and curate structured, FAIR-compliant datasets suitable
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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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, 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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@astro.uio.no ) and Prof. Hans Kristian Eriksen (h.k.k.eriksen@astro.uio.no ). The main goal of this position is to implement a novel Bayesian re-analysis pipeline for Planck HFI in the Commander pipeline, and
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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
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will work with Dr. Baladandayuthapani and his research group in developing statistical and computational methods for large-scale, high-dimensional, complex-structured biomedical data. The research topics
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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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conducting solid oxide cells (E) Skills & Abilities Practical experience of applying computational techniques to the modelling of microstructure in solid oxide cell technologies (e.g. FEM, Gaussian, Bayesian
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.’ The post requires strong quantitative skills to support multi-level epidemiological analyses, causal inference, risk factor estimation, health inequalities modelling and complex dataset construction