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career scientist with background in organic geochemistry, statistics, and Bayesian modeling to pursue analyses of paleoclimate biomarker data. The ideal candidate should be proficient with both laboratory
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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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to implement advanced computational pipelines, including machine learning, deep learning, Bayesian inference, and probabilistic mixed membership modeling for innovative research. · Contribute
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models; 2. Statistical methods, analysis, and inference for large-scale computational simulator applications; 3. Uncertainty representation, quantification and propagation; and 4. Scalable data science
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performance. The salary is commensurate with experience. Applications are invited from individuals who are interested in applying experimental psychology and Bayesian computational modeling to understanding