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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
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and Bayesian methods. Knowledge of statistical software, particularly R. Strong statistical programming skills. Understanding of clinical trials. An ability to work well both on own initiative and
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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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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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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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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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The relationship between the information-theoretic Bayesian minimum message length (MML) principle and the notion of Solomonoff-Kolmogorov complexity from algorithmic information theory (Wallace and
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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
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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