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computing (HPC) and parallel processing to enable the analysis of massive datasets. Experience in advanced statistical inference (e.g., Bayesian statistics, spectral methods) for extracting robust patterns
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Chekouo and his collaborators within and outside the University of Minnesota. The research will focus on the development of Bayesian statistical/machine learning methods for the data integration analysis
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-based proteomics to identify host factors essential for EBV persistence. Our goal is to uncover novel therapeutic targets for EBV eradication and the treatment of associated malignancies. Key
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. These discoveries help identify previously unknown disease-modifying molecular targets and develop innovative strategies to improve animal and human health. Education We provide comprehensive education for
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illnesses. These discoveries help identify previously unknown disease-modifying molecular targets and develop innovative strategies to improve animal and human health. Education We provide comprehensive
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. Experience with structural biology (cryo-EM or crystallography), chromatography, mass spec-based targeted metabolomics, assay development, lipid biochemistry or immunology would be considered assets. The lab
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at the intersection of systems neuroscience and computational modeling. Our lab is broadly interested in Bayesian inference, perception, multisensory integration, spatial navigation, sensorimotor loops, embodied
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mechanisms that lead to cognitive decline in rodent models of Huntington’s disease (HD). We perform intracerebral injections to achieve silencing of specific targets in the brain to explore their impact in
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sediments of the Great Lakes (including legacy contaminants such as PCBs and PBDEs), and application of passive samplers for target and nontarget analysis of emerging organic contaminants (including 6PPD