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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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gap between how artificial and real networks learn. The Department of Neuroscience at the University of Minnesota is home to a strong and collaborative community studying motor learning and motivational
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experiments using murine models to study immune regulation and retinal degeneration • Conduct transcriptomic and pathway analyses (e.g., snRNAs-seq, RNA-seq) to uncover regulatory networks • Use molecular and
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musculoskeletal biology; all of these groups offer regular seminars, journal clubs and social/networking events. Responsibilities: • Planning, execution, analysis, documentation and communication of novel research
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between neuromodulation and fMRI. The postdoc will work on the network level perturbation of neurocircuits using high-definition neuromodulation. This postdoc will lead scientific discovery in developing
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scholars specializing in dynamical systems modeling of neuronal networks for work on a BRAIN Initiative-funded R01 in collaboration with Gordon Smith at the University of Minnesota and Audrey Sederberg
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plasticity metrics derived from functional MRI data. Investigate developmental differences in infant brain functional networks Support generation and testing of improvements for code bases for the analysis
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., gene knockdown, qPCR, antigen presentation assays) to dissect pathways of cell death sensing • Conduct transcriptomic and pathway analyses (e.g., RNA-seq) to uncover regulatory networks • Generate
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-foundation-launches-collaboration-on-ecological-neuroscience/ ). Collaborative Network You will engage with a global team of scientists, including but not limited to: • Jan Zimmermann (University of Minnesota
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experiments pertaining to the role of transcription factor mediated networks to direct the fate of stem and iPS cells to a mesodermal fate (i.e. cardiac, endothelial, skeletal muscle); and conducting research