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Field
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general predictive modeling methods, including model design, parameter estimation, sensitivity analysis, and model evaluation. An understanding of data acquisition and curation methods for real-world data
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general predictive modeling methods, including model design, parameter estimation, sensitivity analysis, and model evaluation. An understanding of data acquisition and curation methods for real-world data
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that connect animal health, data analysis, and human behavior. Design and carry out field studies, collecting and analyzing large data sets. Use and evaluate AI tools to support research and training. Create
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monitoring and electric vehicle battery tracking. Integrate environmental and energy sensors with real-time data collection and analysis systems. Deploy prototypes in the field and optimize their performance
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programs. For more information about working at the University of Arizona and relocations services, please click here . Duties & Responsibilities Conduct integration, analysis, and interpretation of large
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publications in immunology • Strong background in experimental design, statistical data analysis, and data visualization • Prior mentoring experience and capacity for collaborative research across disciplines
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data visualization skills Self-motivated, scientifically mature, attentive to details Ability to acquire new biostatistical techniques by self-learning Able to work independently and in collaboration
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management, and implementation/coaching supports. Methods expertise in one or more of the following areas: survey data analysis; data visualization; research-practice partnerships; conducting applied research
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genomics/transcriptomics data Experience in large scale data analysis, data visualization and data integration Experience in the following topics will be considered an advantage: Analysis of transcriptomics
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single-cell RNA-seq data processing and analysis pipelines (such as Seurat, Scanpy, etc.) Proficiency in R or Python for statistical computing and visualization Familiarity with dimensionality reduction