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on applying, developing and implementing novel statistical and computational methods for integrative data analysis, causal inference, and machine/deep learning with GWAS/sequencing data and other types of omic
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of the analysis methods Advise the PI on the integration of statistical analysis into the manuscript(s) Other duties to be assigned (10%) Participating in other lab activities, such as the weekly lab meeting
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teaching methods to science or mathematics teaching at the college level • Masters in a science (e.g., biology, chemistry, chemical engineering, physics) or mathematics field or equivalent • Experience with
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advanced training in teaching courses within the quantitative methods area. The training experience is intended to competitively position the Teaching Scholar for future employment in teaching-focused
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modeling for neurobiology, including finite element modeling (FEM) or system identification methods in medical applications About the Department The Department of Biomedical Engineering is an academic unit
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analysis ●Assist with supervision of trainees and their interactions with study methods and data ●Contribute substantially to the preparation of manuscripts via literature review, data analysis and
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methods in education, the practice and science of counseling psychology, school psychology, and special education. Faculty and students provide leadership and consultation to the state, nation, and
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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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experimental and computational methods for integrated multi-omics studies. About the Role · Lead independent research projects investigating somatic mosaicism in various tissue types of human or mouse models
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electrophysiology or optical stimulating/recording methods. •Experience working with awake, behaving rodents is essential, which could include work using pharmacologic manipulations. •Prior experience in highly multi