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Field
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well as experience working with probabilistic (Bayesian) methods and statistical modelling. Strong writing and programming skills are essential. A proven record of publishing in high-quality journals and presenting
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Google Earth Engin, R, Python, and STAN (e.g., deep learning, Bayesian regression models, spatial analyses), and running analyses on a high-performance computing cluster. Demonstrated record of publishing
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applications. The project aims to address fundamental theoretical questions related to the representation and measurement of the polarization state, as well as the use of Bayesian and/or statistical learning
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. Applicants with experience in Bayesian modeling, spatial statistics, mathematical modeling, data integration, uncertainty quantification and/or machine learning are encouraged to apply. The postdoc will also
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to observe next. By combining Bayesian inference, probabilistic modeling, and machine learning, the project aims to make Arctic observations more efficient, intelligent, and impactful. You will integrate field
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appreciated. - Advanced statistical modeling (GLMMs, state-space models, stochastic Bayesian programming) in R - Experience with bioinformatics, if possible experience in the use of RAD-seq and/or lcWGS data
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parameter estimation Knowledge of advanced Bayesian methods and samplers, machine learning approaches to signal processing; additionally other methods such as simulation-based inference Good computing skills
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, differential equation-based models and/or Bayesian phylogenetics. The annual base salary range for this position is from $77,976 to $95,014, and pay offered will be based on experience and qualifications
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Bayesian framework and two specific proposed lines of research: (1) constructing suitable priors via neural networks approximations, and (2) enhancing the sensitivity and efficiency of posterior diagnostics
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the analysis of massive datasets. Extensive experience in signal processing and advanced statistical inference (e.g., Bayesian statistics, Fourier analysis) for extracting robust patterns from noisy, high