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Field
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the Department of Defense. Training will be provided through KUMC. The fellow will have access to an integrated and comprehensive research environment for the clinical trial and related research activities. Prior
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to conduct research on developing and using machine learned parameterizations developed from ocean-data assimilation increments. The goal is to develop parameterizations of unresolved processes that will
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literal contributions to the scientific community. CORE JOB FUNCTIONS Develops and implements advanced computational and machine learning methods for the analysis of large-scale omics data, including
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have experience expertise in one or more of the following: MRI pulse sequence design (GE HealthCare EPIC Preferred) Programming in Python and C/C++ Machine learning with PyTorch Excellent writing skills
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the statistics of tropical cyclones over the past millennium, recent historical era and the coming centuries, using a combination of high-resolution climate modeling, machine learning/artificial intelligence (ML
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individually and in an integrative manner using analytic techniques that range from traditional statistics to novel statistical, machine learning and network/systems biology approaches. Initial data queries will
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for soil health mapping. · Collaborate with our interdisciplinary team to develop machine learning approaches for mapping and modeling statewide soil health in Missouri. · Conduct literature
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analysis, probabilistic modeling for deterioration processes, and decision-making under uncertainties is highly desirable. Experience in applying machine learning and performing Bayesian updating is highly
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may be desired; experience in phlebotomy may be desired; experience performing basic laboratory procedures to vital signs may be desired; computer skill required with knowledge of database software
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applied to amorphous materials. You have familiarity with artificial intelligence (AI) and machine learning (ML) methodologies and interested in advancing these tools for accelerating the analysis