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Field
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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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and Bayesian methods) and population studies (nonlinear mixed-effects modeling methods) and hypothesis testing Comprehensive knowledge of the concepts underlying physiological modeling and systems
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learning, path planning, and Bayesian optimization. Plan and perform experiments, oversee research performed by students and research technicians, and mentor and train postdoctoral fellows, graduate students
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tenured/tenure-track faculty and nine full-time instructors. Current research areas of the faculty include survival and reliability analysis, Bayesian statistics, latent variable methods, item response
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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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University of California, Los Angeles | Los Angeles, California | United States | about 2 months ago
and carbon cycle model-data integration using the CARDAMOM Carbon-Water Bayesian model-data integration framework. The candidate will help advance global land biosphere estimates of biomass, water
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received by November 1, 2025. Preferred skills: Demonstrated experience in modeling and applied statistics including machine learning, Bayesian statistics, multivariate statistics, model assisted estimation
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statistical analyses including generalized linear model, multilevel modeling, data mining, survey methodology and Bayesian influences. (Required) Demonstrated experience working on collaborative research
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include Bayesian data analysis, nonparametric statistics, functional data analysis, spatio-temporal statistics, and machine learning/artificial intelligence. Many of our projects involve dynamic processes
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. • Experience with machine and deep learning modeling approaches and developing Bayesian models. • Multidisciplinary skills to bridge fields such as plant disease ecology, remote sensing data, and geospatial