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skills Preferred Qualifications: Familiarity with the CHRD, related software, and research methods Knowledge and/or experience reading a second language Strong interest in international business, corporate
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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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data, as in Mendelian randomization and TWAS. In addition to new methods development and evaluations, the job responsibilities include software development (mostly in R, or in Python/TensorFlow/Keras
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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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12.5% effort Post-Doctoral Associate position. Work in the IGDI-Lab will include supporting scale formation for the IGDI-Lab measures, including defining calibration tables per form (Raw to Rasch tables
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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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Assistant (Lab technician). The research of the lab focuses on RNA regulatory network on metabolic diseases such as obesity and diabetes. We are seeking a motivated and hardworking individual with excellent
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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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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