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Expertise in machine learning, including building and deploying prediction models Strong data science coding skills in programs and languages such as Python, R, Stata, and SQL Experience with research in
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modeling, inference, and compressed sensing. Experience deriving biological insights from large datasets, including mass-spectrometry and DNA-sequencing datasets. Proficiency in Python or another general
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
Python * Experience with version control systems like Git * Experience working as part of large-scale research, scientific, medical, or academic projects * Experience working in, or in support of, public
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Python is advantageous. Practical laboratory skills and an enthusiasm for experimental work are highly valued. Participation in teaching activities (e.g., supervising practical courses or tutorials) may
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not have exceeded two years previous work at the postdoctoral level at start of appointment. Coding experience in a scientific computing language is desirable (e.g., Python, MATLAB, R, IDL, Fortran, etc
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primates or humans – Theoretical neuroscience, machine learning, or AI • Proficiency in Python, MATLAB, or equivalent data‑analysis frameworks. • A passion for big‑picture questions, open science, and
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experience with large-scale human datasets, machine learning, statistical genetics, causal inference, multi-omics, cardiovascular imaging, programming in Python or R, and scientific writing. Prior experience
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designing machine learning pipelines, building web applications or tools, and creating and maintaining visualization dashboards. Trainees should be comfortable with: · SQL, R, and Python
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related to the microbiome/metabolome. Superb quantitative background, strong coding skills (e.g., Python, R, MATLAB). Strong computer skills, experience with databases and scientific applications, and
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Matlab, Python, and/or R programming languages is a plus. Knowledge, Skills and Attitudes: Skill in collecting, organizing, and analyzing data. Analysis of fMRI and MR spectroscopy data. Acquiring