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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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., SPSS, R, Mplus) and data management systems (e.g., REDCap, Qualtrics). Experience contributing to or managing federally funded research projects. Additional Candidate Instruction Applicants should submit
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, such as R, SPSS, Stata, MAXQDA, and Nvivo. Aid in the analysis of data to derive insights into best practices for practitioners and recommendations for policy makers. Ability to present research findings
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software to create maps. Experience with data entry and data QA/QC protocols to support ecological and forestry research. Knowledge of Microsoft Excel highly preferred, familiarity with ArcPro and R also
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- and micro-flow LC-HR-MS/MS for comprehensive multi-omics analysis. Proficiency in Python and R (essential) for data analysis, modeling, and visualization. Ability to work effectively within diverse
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in genome and exome sequencing analysis. Expertise in bulk and single-cell RNA sequencing analysis. Proficiency in programming languages such as Python and R for data analysis, modeling, and
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, accessibility, or universal design. Experience with AR/VR tools, programming/coding (Python, R, Unity, or JavaScript), or community-engaged research. Experience working in interdisciplinary teams and multi-site
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. Preferred Qualifications Knowledge of aging-in-place, accessibility, or universal design. Experience with AR/VR tools, programming/coding (Python, R, Unity, or JavaScript), or community-engaged research
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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