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research encompasses (but is not limited to) identifying effective tutoring practices, running experiments on AI tutors in simulated and real-world environments, fine-tuning AI models to classify qualitative
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: Leading system mapping for multiple zoonotic systems, including Hendra virus in Australia and Nipah virus in Bangladesh Helping organize, and participate in, Participatory Model Building with local
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will lead research at the intersection of crop-switching, environmental resilience, and agrifood sustainability. The candidate will develop and apply spatial analysis, remote sensing, and modeling
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of crop-switching, environmental resilience, and agrifood sustainability. The candidate will develop and apply spatial analysis, remote sensing, and modeling to evaluate the technical, environmental, and
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associate to develop deep learning algorithms: 1) to model 3D protein complex structures and impact of mutations/PTMs on protein structure and interactions; and 2) to dissect functional elements (e.g
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community as a model for hypothesis testing in molecular evolution. The individual will use methods such as RNA-seq to identify genes and regulatory sequences that drive plant toxin detoxification, transport
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multilevel models is valued. Additional skills with mixed methods research on related topics, including surveys, interviews, focus groups is preferred, but not required. Must have expertise in using
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. Strong analytic skills such as structural equation modeling, multilevel modeling, longitudinal data analysis, and/or categorical analysis (e.g., growth mixture modeling) using R, Mplus, and/or SAS
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strong quantitative skills and demonstrated experience in applied econometric analysis, large-scale data integration, and global or regional economic modeling, as well as an interest in climate risk, soil
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activities. The successful candidate will conduct comparative and experimental studies on transcriptomic responses to insect diet and toxins, utilizing the milkweed-insect community as a model for hypothesis