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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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events, mentoring junior scholars, and supporting ongoing field-building efforts by Dr. boyd’s team. Requirements PhD in the social sciences, STS, history, or relevant field and have conducted significant
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] dataset), along with open-source applications that support data processing and analysis grounded in Responsible AI principles. A core part of the NTO’s mission is to develop open data and computational
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the research group and will work with collaborators at other institutions (nationally and internationally) The successful candidate should have the following qualifications: PhD in Epidemiology, Data Science
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events, mentoring junior scholars, and supporting ongoing field-building efforts by Dr. boyd’s team. Requirements PhD in the social sciences, STS, history, or relevant field and have conducted significant
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internationally) Requirements: The successful candidate should have the following qualifications: • PhD in Epidemiology, Data Science, Applied Mathematics, Computer Science, Statistics, Public Health, Economics or
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Required: PhD in Biomedical Engineering, Bioengineering, Computer Science, Biophysics, Computational Biology, Molecular Biology, Genomics, or a related field Proficiency in computational data analysis and
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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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approaches, including machine-learning methods, that improve the identification or enumeration of overlapping vocal individuals. Array localization, spatially explicit capture-recapture, distance sampling