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team-working within work Proven ability to work without close supervision Desirable CriteriaExperience with Bayesian statistics Experience working with a range of geochemical proxies, including
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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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open-ended position. Applicants are invited from any area of applied statistics, including statistical or actuarial data science. Those working in actuarial science, Bayesian statistics, statistical
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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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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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multivariate methods, network analysis techniques, Bayesian methods, power and sample size calculation, statistical methods for genomics and sequence analysis (including next generation sequencing platforms
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required. Knowledge of statistical modelling and Bayesian methods. Knowledge of statistical software, particularly R. Strong statistical programming skills. Understanding of clinical trials. An ability
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- based neural networks, Bayesian statistics, and text analytics are a must. Nice to Have: Experience developing and integrating APIs for healthcare systems to ensure seamless interaction with AI models
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model fitting, including Bayesian model fitting. Experience of management and analysis of large multidimensional real world data sets. What we can offer you The opportunity to continue your career at a
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projects, including: The post-holder will run numerical models that simulate the dispersion of greenhouse gases through the atmosphere. These models will be used, in Bayesian inference frameworks