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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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projects ranging from score-based generative models, energy-based models, Bayesian analysis of graph and network structured data, highly multivariate stochastic processes; with data applications ranging from
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. The work will apply state-of-the-art three-dimensional atmospheric chemistry and circulation models, together with advanced statistical techniques (optimal Bayesian, Markov Chain-MonteCarlo, etc.) to solve
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in modern scientific computing -Excellent communication and collaboration skills Preferred -Experience with simulation-based inference and Bayesian methods -Familiarity with cosmological simulations
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. Prior exposure to experimental data from photon-counting or time-resolved detectors. Experience with Bayesian methods, uncertainty quantification, or real-time data processing. Familiarity with
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projects ranging from score-based generative models, energy-based models, Bayesian analysis of graph and network structured data, highly multivariate stochastic processes; with data applications ranging from
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 6 hours ago
quantification, Bayesian statistics, and multivariate optimization; must have excellent writing and communication skills. Prior space systems engineering and V&V experience, including planning and execution
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to experimental data from photon-counting or time-resolved detectors. Experience with Bayesian methods, uncertainty quantification, or real-time data processing. Familiarity with distributed computing or HPC
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project is to develop scalable and privacy-preserving Bayesian computational algorithms. The position is intended for two to three years, with an initial one-year appointment renewable contingent upon
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to offer. Qualifications: Required: PhD in ecology by start date Experience in plant phenology, biogeography, and spatial and temporal modeling (Bayesian and frequentist) Expertise in R or Python, GIS, big