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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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experience should involve preparing material for specialized analysis including PCR analysis. Experience processing and analyzing tissues. Experience working with laboratory animals. Basic computer skills are
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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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applicable public health and safety procedures. This position is a hybrid of remote and in-person work, located on the campus of UMN Medical School Duluth, with workstation, landline telephone and computer to
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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
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tools. * Familiarity with tensorflow and/or pytorch. * Demonstrated ability to design and implement machine learning techniques and algorithms. * Demonstrated expertise in the Linux computing environment
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related field Excellent organization, planning, interpersonal skills Knowledge of computer hardware and software, including Excel spreadsheets Written, verbal and comprehension abilities in