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radio interferometry data, particularly very long baseline interferometry. Experience with or skills relevant to statistical modelling and Bayesian inference. Demonstrated familiarity with the fields of X
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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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will apply predictive, Bayesian modelling for predicting performance of microbial consortia based on mass transfer, metabolic pathways and proteomic analysis. The work plan will also include developing
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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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design, computer experiments, sequential analysis, shape-constrained inference, time series, and Bayesian analysis. In applied mathematics, these include information theory, coding theory, control theory
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devising successful models, techniques and methods (e.g., regression modelling, causal inference, survival analysis, Bayesian approaches, risk factor estimation) Extensive experience and achievement in
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publication(s) The following qualifications will count in the assessment of the applicants: Familiarity with Bayesian estimation techniques Familiarity with machine learning methods Proficiency in IRT Personal
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: Familiarity with Bayesian estimation techniques Familiarity with machine learning methods Proficiency in IRT Personal skills A collaborative, friendly, and team-oriented style of work Ability to join