81 machine-learning-and-image-processing-"RMIT-University" 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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biology techniques including qPCR, cloning, western blotting, and CRISPR or shRNA-based gene knockdown • Experience with RNA-seq or other transcriptomic analyses • Experience in confocal imaging
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are ones with objectives of modeling flow and transport processes in the vadose zone using various data analysis and advanced modeling approaches (statistical and machine learning methods). The candidate
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, immunofluorescence, PCR, qPCR, cell-based assays, fluorescence calcium imaging. • Perform data acquisition and analysis, grant and manuscript preparation, and presentation of findings at weekly lab meetings
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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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: • Experience with antigen-specific immune monitoring (e.g., MHC class I tetramers) • Experience in confocal imaging, ELISA, and viral assays • Experience with bioinformatic workflows and interpretation of immune
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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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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
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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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Routine mammalian cell culture, transfection using sterile techniques. Preferred qualifications: • Immunofluorescence microscopy: sample preparation, immunofluorescent microscopy experience, image analysis