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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 1 hour ago
-dimensional datasets. Knowledge of experimental design, statistical analysis, model validation, and reproducible computational research practices. Evidence of scholarly productivity through peer-reviewed
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required along with knowledge of molecular biology, computer programming, statistical analysis and genome evolution. Mentoring experience is also required. Certifications/Licenses Required Knowledge, Skills
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Qualifications PhD in Mathematical Economics and Social Science Candidates should have experience in at least one of the following areas: Statistical programming and data analysis using R, Python, or Matlab
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 28 days ago
Requirements Doctoral degree in statistics, industrial and systems engineering, electronic engineering, computer science, or related field. Required Qualifications, Competencies, and Experience Doctoral degree
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statistical analysis and hypothesis testing Strong publication record Our University Community Physical Demands Driving Requirements This position will never drive a university owned vehicle. Salary Range
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Chekouo and his collaborators within and outside the University of Minnesota. The research will focus on the development of Bayesian statistical/machine learning methods for the data integration analysis
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with hands-on experience in nanoparticle synthesis, surface functionalization, and chelation chemistry. Proven analytical abilities, including proficiency in data analysis software and statistical
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and statistical analysis skills for experimental data interpretation. Rigorous scientific documentation practices, including maintenance of detailed laboratory notebooks and research records. Excellent
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, proteomics or whole-genome sequencing data, is essential, along with strong quantitative and computational skills. Experience using R, Python or other statistical programming languages to analyse biological
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understanding of reproducible research, statistical analysis, and the integration of sequence data with associated clinical or biological metadata, alongside evidence of contributing to scientific publications