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methods of data analytics (e.g., statistics, stochastic analysis, Bayesian statistical analysis), physically-based hydrology and water quality models, and the use of machine learning tools for modeling flow
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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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statistical methods to agronomic research, including mixed models, geospatial statistics, multivariate analysis, and machine learning - Must possess and maintain an active and valid driver’s license Preferred
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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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, implement, and evaluate computational models that assimilate 2-photon data (60%) Use a computer programming language to create novel neural network simulations (models) that include realistic simulations
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technologies and how their technology use impacts their relationships and well-being, and on the application of machine learning in family and developmental research. The Post-Doc will contribute to Dr. Sun’s