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on applying, developing and implementing novel statistical and computational methods for integrative data analysis, causal inference, and machine/deep learning with GWAS/sequencing data and other types of omic
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methods of data analytics (e.g., statistics, stochastic analysis, Bayesian statistical analysis), physically-based hydrology and water quality models, and the use of machine learning tools for modeling flow
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host interactions through network analysis, machine learning. - Ability to map microbial genes to biochemical pathway analysis - Excellence in research, communication and collaboration skills, as
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degree -Prior course work in computer analytics and advanced mathematics -Programming experience preferred: SAS, R, STATA and Python -Eligibility for certification using restricted CDC data sets Preferred
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conducting clinical or preclinical proof-of-concept studies Preferred: Experience in physiological signal processing and the application of machine learning to biomedical data Background in computational
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ability, strong mathematical background, computer programming experience. About the Department Neuroscience is the scientific study of the nervous system. It is an interdisciplinary science that
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statistical methods to agronomic research, including mixed models, geospatial statistics, multivariate analysis, and machine learning - Must possess and maintain an active and valid driver’s license Preferred
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computer skills for accurate data collection using various hospital EMR systems (primarily EPIC systems) and willingness to learn various Data Capture systems (WebDCU, RAVE, Red-Cap) ability to work on site
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• Good working knowledge of MS Excel, Word, PowerPoint, as well as fluency in computer techniques and software for experimentation and analysis • Ability to self-motivate, multitask and make intellectual
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analyses Familiarity with AI/machine learning/deep learning frameworks Familiarity with Microsoft Office Software such as Word and Excel; Coding experience in R. Work with plant materials in dusty/dirty