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: Cuantificación de Incertidumbre Bayesiana (Bayesian Uncertainty Quantification, BUQ) Appl Deadline: 2025/10/30 11:59PM * (posted 2025/09/08, listed until 2025/10/30) Position Description: Apply Position
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the surrogate forward models with a Bayesian inverse modeling framework to achieve real-time or near-real-time uncertainty quantification, such that we can efficiently resolve the uncertainties rising from rock
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-country survey datasets for comparative analysis. Conceptualize and refine a theoretical framework integrating intersectionality and stigma processes. Develop and code a Bayesian meta-regression to pool and
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spatial and temporal scales, leveraging cutting-edge hierarchical Bayesian modeling approaches. The Fredston Lab uses large datasets, theoretical models, and a range of statistical tools to predict marine
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, such as Bayesian approaches and fossilized birth–death models, to reconstruct robust phylogenies and estimate divergence times. It also investigates macroevolutionary dynamics, including variation in
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measurements are most informative and guiding where, when and how to observe next. By combining Bayesian inference, probabilistic modeling, and machine learning, the project aims to make Arctic observations more
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spectrographs with various spectral resolutions, operating from 0.5 to 28 µm. Our group has developed the Bayesian modeling tool FORMOSA (Petrus et al. 2023). It allows the inference of low-resolution (R = λ/Δλ
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Bayes factor hypothesis tests in factorial designs. What are you going to do The envisioned projects will focus on the following activities related to Bayesian inference in factorial designs: Construction
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, macroinvertebrate collection, and stable isotope analysis. The successful candidate is expected to have extensive experience in aquatic ecology, coding in R, and an ability or willingness to learn Bayesian modeling
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, macroinvertebrate collection, and stable isotope analysis. The successful candidate is expected to have extensive experience in aquatic ecology, coding in R, and an ability or willingness to learn Bayesian modeling