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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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work closely with CFN Electron Microscopy group members and computer scientists at Brookhaven. You will be professionally mentored by Dr. Judith Yang and Dr. Sooyeon Hwang and receive guidance from Prof
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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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that you will work closely with CFN Electron Microscopy group members and computer scientists at Brookhaven. You will be professionally mentored by Dr. Judith Yang and Dr. Sooyeon Hwang and receive guidance
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, enhanced by machine-learning and data-driven analysis techniques. Additionally, the study will encompass electrically triggered events that mimic the voltage-based signaling of biological synapses
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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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excellent verbal and written communication skills, attention to detail, and demonstrated computer efficiency. In addition, the candidate should have strong organizational skills, the ability to successfully