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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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, 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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for performing image acquisition and reconstruction (10%) • Create and use computer programs to perform simulations of the developed technologies, for the purpose of testing and optimizing their performance (10
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interest in the use of machine learning techniques to enable new analysis strategies, as well as the application of deep understanding of the detector to enable novel physics studies. The group also has a
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in the University of Minnesota. The research will focus on applying, developing and implementing novel statistical methods for causal inference, integrative data analysis or/and machine/deep learning
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Code 9546 Employee Class Acad Prof and Admin Add to My Favorite Jobs Email this Job About the Job Required Qualifications: • PhD in Electrical & Computer Engineering, Computer Science, Biomedical