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knowledge gained from climate models to applied researchers and decision makers are encouraged to apply. A Ph.D. in physical geography, statistics/biostatistics, environmental, climate, atmospheric, physical
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in R for conducting multivariate statistics and deep learning analyses using discrete and continuous phenotypic data; Assist lab members on conducting Python-based Bayesian inference analysis
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of tools and public goods. Requirements Applicants must have (or expect to have) a PhD in the social sciences, statistics, computer science, or related fields, and their interests must fall at the technical
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information. Responsibilities will include establishing and managing collaborative, multisite research projects; conducting advanced statistical analysis on complex data sets; disseminating research through
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letters) for at least 2 references (we will contact references for letters at a later stage). PREFERRED - Experience working with REDCap - Basic statistics training (e.g., ANOVA, t-tests, use of SPSS and R
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Ph.D. in quantitative ecology, climate sciences, population biology, applied mathematics, statistics, or an equivalent discipline. Additional experience in climate, meteorological, or environmental data
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. Deike, Breaking wave statistics with a multilayer model, J. Fluid Mech. 968 [2023] A12). The projects have overlapping synergies, and the postdoctoral researchers will interact with graduate students
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, Monte Carlo simulations, statistical mechanics, fluid mechanics, computer programming (e.g., C++, Python), polymer theory, molecular modeling (e.g., of proteins and nucleic acids), coarse-grain and
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are particularly interested in candidates with computational modeling and strong statistical skills. Other duties include contribution to other ongoing center projects, preparation of and participation in workshops
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contact references for letters at a later stage). PREFERRED - Experience working with REDCap - Basic statistics training (e.g., ANOVA, t-tests, use of SPSS and R) - Experience designing research studies and