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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 days ago
- Proficiency in programming (e.g., Python, R, or similar). - Experience with large-scale omics data analysis. - Excellent communication skills. Preferred Qualifications, Competencies, and Experience - Solid
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coding experience with e.g. Python/Matlab/R Practical experience with High Performance Computing, and scientific programming and a willingness to learn to work with high-performing computing systems
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fractionation (i.e. surface biotinylating, gradient centrifugation, and be proficient in advanced data analysis (i.e. R, Python). Excellent analytical, organisational, and problem-solving skills are essential
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, longitudinal data analysis, and/or categorical analysis (e.g., growth mixture modeling) using R, Mplus, and/or SAS. Preferred Qualifications: Experience with analyzing multi-wave longitudinal and intensive
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. • Demonstrated ability or potential to lead graduate student research. • Experience with Matlab, R, Python, C++, or Julia. • Experience collecting data from human subjects. Overtime Status Exempt: Not eligible
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(e.g., Bioconductor, Galaxy, KEGG, Reactome, STRING). Proficiency in Python, R, and Unix/Linux-based environments for high-performance data analysis. Knowledge of biological network inference, causal
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CBS - Postdoctoral Position, Artificial Intelligence Applied to Metabolomics for Health Applications
classifiers). Proficiency in programming languages such as Python, R, and experience with bioinformatics pipelines. A proven track record of scientific publications in metabolomics, AI, or computational biology
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R documented excellent and intuitive graphical presentation of data analyses for presentation and publication excellent English language skills (speaking and writing) comprehensive presentation
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innovative peer-reviewed research publications Mentor Postdocs through the grant development process, including applications for F, K, or R-series awards and other career development mechanism Required
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or soon after Advanced training in econometrics, empirical archival methods, and causal inference methods Strong programming skills in Stata, SAS, R, Python, or similar software Experience in teaching