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combination of analyses of root distributions, periodic and high frequency soil moisture data, and physical and chemical soil data. Specifically, the individual will: Work with physical or machine learning
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Computational Methods: ab initio molecular dynamics simulations, development of machine learning, and reaction rate theory and modeling Research: Project design, execution of calculations, data analysis
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packages such as SAS, R, or Mplus. They will have the opportunity to visualize data for multiple audiences. They will learn to prepare and share datasets (along with data dictionaries) and scripts in public
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soil moisture data, and physical and chemical soil data. Specifically, the individual will: Work with physical or machine learning models to explore drivers of soil structure, preferential flow, depth
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opportunity to visualize data for multiple audiences. They will learn to prepare and share datasets (along with data dictionaries) and scripts in public data repositories. In addition, they will help pre
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Longitudinal Modeling Analysis techniques for school-based research data. Professional Skills. Acquire strategies to write grants and manage research projects. Develop methods and skills to effectively