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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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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 6 hours ago
. 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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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
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-development and refinement of conceptual models; devising management scenarios; building network models in one or more platforms (e.g., loop analysis/qpress; fuzzy cognitive maps/Mental Modeler; Bayesian belief
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to implement advanced computational pipelines, including machine learning, deep learning, Bayesian inference, and probabilistic mixed membership modeling for innovative research. · Contribute