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Field
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programming skills in Python and experience in developing and documenting reproducible research code (e.g. using PyTorch or comparable frameworks). Practical experience with AI-assisted development and coding
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techniques, and stochastic optimization. Additional knowledge of machine learning and experience with programming in Python and PyTorch would be considered an advantage. You are experienced in conducting
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is a plus. Candidates should also demonstrate strong skills in Python (for ML/NLP tasks) and R (for statistical modeling or data analysis), as both will be actively used in the research workflow
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in research software development using R or Python and a track record of contributing to publications and presentations in leading statistics or machine learning journals and conferences are essential
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Excellent communication and writing skills Certifications/Licenses Required Knowledge, Skills, and Abilities Preferred Qualifications Experience handling very large datasets Knowledge of R and Python, and
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days ago
epidemiologic methods, causal inference, and statistics. Proficiency required in statistical software (e.g., R, SAS, Python). Preferred Qualifications, Competencies, and Experience - Special Physical/Mental
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methods, omics data analysis, and spatial tools is highly valued. Programming expertise in Python and/or R is essential. As a person you demonstrate high ambitions. You are equally innovative – and result
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Engineering, or a closely related field. * Proficiency in MATLAB or Python. * Prior experience with Neuroimaging analysis (using FSL, AFNI, SPM, etc.) * At least one journal publication as first author
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islet imaging assays, in vivo assays of metabolism (e.g. glucose tolerance tests), and developing novel machine learning models (in Python and R). Experience with mammalian cell and tissue culture
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. Proficiency in Python, MATLAB, or R. Strong quantitative and analytic skills. Preferred Qualifications Experience with evidence-accumulation models (DDM, sequential sampling, Bayesian models). Experience with