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on developing advanced machine learning models to quantify phenotypic traits of crops, including corn, soybean, and other selected species. These models will leverage data collected from various sources, such as
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students’ academic and mental health outcomes. Job responsibilities include: Conducting descriptive and advanced statistical analyses (including multilevel modeling) on extant and merged datasets using SAS
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degeneration using knock-in models of inherited retinal diseases, and contributing to the development of novel therapeutic platforms. The successful candidate will conduct independent and collaborative research
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using knock-in models of inherited retinal diseases, and contributing to the development of novel therapeutic platforms. The successful candidate will conduct independent and collaborative research
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will contribute to projects focused on developing advanced machine learning models to quantify phenotypic traits of crops, including corn, soybean, and other selected species. These models will leverage
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and manipulating complex data structures, Bayesian modeling, analyzing nested longitudinal data, and who are familiar with techniques for handling challenging data (e.g., highly non-normal distributions
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Materials Characterization; Biomaterials, Bioinspired and Self-Assembled Materials; Energy Materials and Sustainability; Materials for Structural Applications and Extreme Environments; Modeling, Theory and
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Posting Title Researcher II/III: EMT Modeling, Simulation and Analysis for Large-Scale Power Systems . Location CO - Golden . Position Type Regular . Hours Per Week 40 . Working at NREL The National
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locomotion. Apply machine learning and machine vision algorithms to track body and limb movements. Use biomechanical modeling to analyze walking data and fit locomotion models. Operate a force sensor to
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://comartsci.msu.edu/departments/com Bidding eligibility ends October 14, 2025, 11:55 PM Create a Job Match for Similar Jobs About Michigan State University Spartans work every day to advance the common good in uncommon