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-performance computing (HPC). The objective of these fellowships is to facilitate cross-disciplinary, cross-location research through collaborative research on problems of interest to each applicant and amenable
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-performance computing (HPC). The objective of these fellowships is to facilitate cross-disciplinary, cross-location research through collaborative research on problems of interest to each applicant and amenable
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enhance assignment allocation and improve the efficiency and consistency of pharmaceutical quality assessments. Learning Objectives: You will gain experience in data analysis, algorithm development, and the
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crop area and learn basic agronomic, data collection, and plant breeding methodologies in trials and nurseries planted at the USDA-ARS. Learning Objectives: The project assignments will provide you with
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will be a part of data collection, analysis, modeling and simulation for this project. Learning Objectives: Under the guidance of a mentor, you will have the opportunity to learn about drug development
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of dosing strategies — in ways that complement or improve upon conventional development paradigms. Learning Objectives: As a participant, you will have structured learning opportunities in regulatory science
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process improvement concepts within a federal setting. Learning Objectives: Through participation in this mentored research experience, the participant will gain practical knowledge of AI/ML applications
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potential constraints affecting protected species. These activities will support ongoing natural resources management objectives by contributing field-based data, technical observations, and research findings
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potential. Learning Objectives: Through participation in this research project, you will: Develop expertise in advanced characterization of complex pharmaceutical materials by applying complementary
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. Learning Objectives: During the fellowship, under the guidance and mentorship of the mentor and senior research staff, you will: Learn how different environmental stress and biotic factors affect tree