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structures, biological responses, and clinical outcomes; 3) assess the performance of translational statistical approaches under a range of simulated scenarios based on available data. Mentor: The mentor
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, statistical, or mechanistic models to translate nonclinical data (such as chemical structures, sequences, and in vitro functional assays for specific drugs) into safety endpoints (such as cardiotoxicity
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of healthy foods with healthful ingredients. In collaboration with the Grain Science and Industry Department at Kansas State University and the USDA-ARS Grain Quality and Structure Research Unit in Manhattan
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how to adjust model structures/parameters based on calibration data, and evaluate model credibility using validation data; Learn how to execute modeling & simulation to predict various outcomes
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the past four years or anticipated to be complete prior to start of appointment. Preferred skills: Experience in organic synthesis, analytical chemistry, structure-property relationships, material
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theoretical solutions for modernizing infrastructure, focusing on energy efficiency, environmental controls, and biosecurity requirements. Observe and engage in the stages of federal construction and repair
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Objectives: The participant will develop an understanding of cotton fibers as a textile material through chemical transformations and mechanical construction taking into consideration fiber morphology and
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transcriptomics datasets generated by the host laboratory. To develop insecticidal molecules, the Fellow will utilize AI/machine learning, structural and computational biology, bioinformatics, and genomics tools
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areas. These include, but are not limited to: Applying machine learning algorithms to solve real-world problems. Creating and structuring databases for storage, retrieval, and image analysis. Determining
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-products. Current extracts from whole rice, rice bran, and rice hulls will be evaluated for biological activity, followed by purification and structural characterization of active components. This research