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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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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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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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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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and modeling for digital twining Semi-supervised and unsupervised machine/deep learning for damage mapping and quantification Automated data generation through 3D computer graphics and Generative AI
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differences in infant learning. The project is a multi-site longitudinal study conducted at the University of Kansas under Dr. John Colombo in collaboration with the infant laboratory at the University
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Assist with conducting analyses of data from research projects that use the Self-Determined Learning Model of Instruction (SDLMI), the Self-Determined Career Design Model (SDCDM), the Self-Determination
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for knowledge are of paramount importance. The University of Kansas is committed to a learning and workplace environment that is representative of our nation and global society, accepts and values everyone
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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