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expertise of the successful candidate. Potential focal areas could include: Integrated modeling approaches that quantify the inferential contributions of eBird and acoustic monitoring data and inform sampling
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methods and software, including uncertainty methods, spatialized approaches, and integration with dynamic modeling frameworks. Experience applying or developing mechanistic animal models, and/or whole farm
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of comparison, with careful attention to relationships among the data sources and the need for independent validation. Hierarchical models that represent relevant components of the acoustic observation process
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will contribute to a spatialized, dynamic life cycle assessment of ruminant production systems in the Northeast U.S. using integrated assessment models. The candidate will also contribute
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will contribute to a spatialized, dynamic life cycle assessment of ruminant production systems in the Northeast U.S. using integrated assessment models. The candidate will also contribute
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quantitative ecology, applied statistics, or a related field with strong background in statistics and model development. Experience with R and analyzing spatial datasets. Ability to apply quantitative methods
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quantitative ecology, applied statistics, or a related field with strong background in statistics and model development. Experience with R and analyzing spatial datasets. Ability to apply quantitative methods
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problem formulation from solution concepts, integrating models from different disciplines, operating at different time/spatial scales, analyzing the performance, and cost/schedule/risk aspects of the system