80 phd-in-computer-vision-and-machine-learning Postdoctoral positions at University of Minnesota
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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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to lead a project related to the transport of bacteria in porous media and multiphase flow. A PhD degree in engineering or earth science is needed. 75% - Conduct laboratory experiments related
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have a PhD in environmental engineering, earth or environmental engineering, or related fields, with a background in ecohydrology. Experience in ecohydrological modeling and remote sensing is desired
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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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: Competitive wages, paid holidays, and generous time off Continuous learning opportunities through professional training and degree-seeking programs supported by the Regents Tuition Benefit Program Low-cost
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. Expected distribution of duties includes: ● Laboratory benchwork: 75% ● Data analysis, writing, and presentations: 25% Qualifications Required Qualifications: ● A PhD degree in Neuroscience or a related
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you learned about this program. For information about this position, please contact Dr. Mustafa al’Absi at malabsi@umn.edu The University of Minnesota is an equal opportunity educator and employer
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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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• PhD, DDS, DVM, JD, MD or equivalent is required. Preferred Qualifications • Experience with primary airway epithelial cell biology. • Experience with viral vector-based gene therapy for pulmonary
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hydrology and computer science. Any previous success in leading and delivering academic deliverables of using machine learning in solving hydrological problems and experience in spurring interdisciplinary