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or willingness to develop skills in analytical techniques such as GC–MS or related methods for quantifying seed-applied compounds Experience with statistical modeling, experimental design, and multivariate
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-applied compounds • Experience with statistical modeling, experimental design, and multivariate analysis of biological datasets using R, Python, SAS, or related platforms • Ability to integrate laboratory
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-insect 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
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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. Preferred
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abiotic/biotic stressors and secondary metabolite production, particularly in climate-resilient crops like tomatoes. • Proven record of peer-reviewed publications and research productivity. • Ability
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generate new insights on the climate resilience of cotton and the preservation of dwindling irrigation resources. The postdoctoral research associate is expected to lead field experiments, collect data
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Ph.D. in molecular biology, cell biology, biochemistry or neuroscience. Prior research experience in mouse models, and/or membrane trafficking and lysosome biology will be a plus. Interested candidates
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. Integrate the feed chemistry data being developed in a parallel project. Travel to India to help implement the updated model. This would be as needed and no more than two times per year. Conduct a comparative
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. Applicants must have experience in the handling and organization of large data sets. Familiarity with analysis of panel or longitudinal data and working with multilevel models is valued. Additional skills with
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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