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Field
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-based simulation models · Knowledge of Bayesian and Markovian calibration methods · Expertise in evaluating agent-based model outputs · Experience in “debugging” agent-based simulation
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to engage with multidisciplinary teams and external partners. Desirable attributes include experience with spatio-temporal models, machine learning, Bayesian methods, and knowledge of environmental exposure
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uncertainty. Relevant areas include interpretable probabilistic, causal, Bayesian, and knowledge-based networks. While expertise in electromagnetics is not essential, applicants must be willing to broaden
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associated with phenotypic (biomechanical and metabolomics) traits. Estimate locus-specific effect sizes and quantifying genetically-driven phenotypic variations. Develop Bayesian models and/or deep learning
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including, but not limited to: Bayesian statistics, computational statistics, inverse problems, numerical analysis, probability, statistical machine learning, stochastic analysis, and uncertainty
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Probability, Regression Analysis, Multivariate Analysis, Categorical Data Analysis, Optimization, Time Series Analysis, Survival Analysis, Actuarial mathematics, Data Mining and Bayesian Statistics are welcome
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of biofouling processes in marine environments. This role will focus on developing and applying Bayesian statistical models to investigate and predict biofouling patterns to enhance our understanding of how
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for differentiating effectful programs such as gradient estimation of probabilistic programs, implicit function differentiation, compositional Bayesian inference techniques); analyzing what is required (e.g., choice
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of different forms of human, Training in radiocarbon dating and its application to archaeology, pretreatment chemistry, palaeoproteomics and the Bayesian modeling of radiocarbon dates will be given, but prior
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features to behavior using GLMMs/Bayesian models; conduct sensitivity and robustness checks. * Method validation: benchmark alternative pipelines (filters, burst detectors, forward/inverse models); perform