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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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, performing, analyzing, and writing up to report for publication behavioral neuroscience experiments addressing questions of decision-making in rodents Qualifications Required Qualifications: PhD in
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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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in the University of Minnesota. The research will focus on applying, developing and implementing novel statistical methods for causal inference, integrative data analysis or/and machine/deep learning