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on a weekly basis Travel, typically once a year, to present results at conferences Generate first-author publications in peer-reviewed journals reporting novel model elements Document and share open
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on the development of Bayesian statistical/machine learning methods for the data integration analysis of high-throughput imaging and molecular data (i.e., genome, transcriptome, epigenome, and more). The methods would
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-cell and omics approaches, our methods allow for in-depth exploration of intracellular signaling networks and physical interaction networks in health and diseases. Using these methods, we aim
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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 in the areas
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. conducts literature searches for methods and procedures specified by the principal investigator; plans and develops assays to evaluate experimental methods and techniques. (15%)- Manage research results and
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• Skilled in single-cell/population data analysis (e.g., GLMs, decoding) Preferred Qualifications • Background in machine learning or computational modeling (Bayesian methods, neural networks, etc
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of the project. Draft sponsor reports to detail updated activity and sponsor required elements. Qualifications Required qualifications: Applicants must possess a recent MD degree. Experience with data collection
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teams. Familiarity with uncertainty quantification and parameter calibration methods. About the Department The Department of Bioproducts and Biosystems Engineering (bbe.umn.edu) is an internationally
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, qPCR, drug development, experimental design, data analysis and recording primary data and methods used. 5% Research communication Critical analysis of the literature, preparing manuscripts, oral
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electrophysiology or optical stimulating/recording methods. •Experience working with awake, behaving rodents is essential, which could include work using pharmacologic manipulations. •Prior experience in highly multi