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University of Massachusetts Medical School | Worcester, Massachusetts | United States | about 5 hours ago
modeling, or machine learning - Experience with large-scale genomic data analysis (e.g., GWAS, QTL, PRS, or multi-omics integration) Strong programming skills in R or Python; familiarity with Bayesian
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, Medicine, Global Health, Respiratory Medicine). Strong background in epidemiology, clinical research, or biomedical sciences. Proficiency with data analysis and statistical software (e.g., R, SPSS, Stata
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, R) and data visualization techniques Previous experience working in a research laboratory setting, preferably in pediatrics or related fields. Equal Employment Opportunity Statement All qualified
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simulations and perform computational experiments using high-level programming languages (e.g., Python, MATLAB, R, or Julia). Curate and integrate experimental data to calibrate and validate models, including
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the U.S Experience working with spatial data and machine learning models. Strong knowledge of programming languages, such as Python, R . Demonstrated skills in scientific writing, publications, and
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. This must include strong Excel skills. Proficiency in R is a plus. Strong working knowledge of Mplus, in addition to at least one other general statistical package (e.g., SPSS, R, or SAS), is required
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quality control standards Quantitative methodology Stata, SAS, SPSS, MPLUS, R, or MAXQDA In lieu of the education/experience requirement, additional related education and/or experience, above and beyond
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interaction-based experiments. Proficiency in Stata, R, or SPSS. Preferred Competencies Outstanding verbal, written, presentation, and organizational skills. Working Conditions This position is currently
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and/or R knowledge will be given priority. This individual will act as the lead team member for a specific administrative data set and will assist other lead members on their assigned administrative
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laboratory with seven Nobel Prize-winning discoveries, 37 R&D 100 Awards, and more than 70 years of pioneering research. The Lab is primarily supported by the U.S. Department of Energy's (DOE) Office of