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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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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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Institute (https://cse.umn.edu/aiclimate). The role involves building knowledge-guided machine learning (KGML) models for sustainable agricultural practices, developing AI-ready benchmark datasets, and
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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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the results (e.g. worksheet, graphs, tables, etc.) and assists in developing appropriate computer programs. o Analysis of data obtained as a result of experiments performed, and preparation of laboratory
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collection and in communicating with individuals and groups in a computer networked environment. About the Department The Department of Dermatology is committed to providing excellent patient care, conducting
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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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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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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