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approaches, including machine-learning methods, that improve the identification or enumeration of overlapping vocal individuals. Array localization, spatially explicit capture-recapture, distance sampling
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, spatial statistics, machine learning approaches, large ecological datasets Previous experience with passive acoustic monitoring and/or eBird data Familiarity with ecology and/or ornithology Demonstrated
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transdisciplinary teams to quantify synergies and tradeoffs between environmental impacts, ecosystem services, and the social and economic dimensions of food systems. Responsibilities Conduct spatially explicit
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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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spatial scales (e.g., FeederWatch, NestWatch) while creating dynamic educational content and web applications through which the public can better understand the natural world. The Cornell Lab is widely
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spatial scales (e.g., FeederWatch, NestWatch) while creating dynamic educational content and web applications through which the public can better understand the natural world. The Cornell Lab is widely
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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