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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 16 hours ago
: Python, Perl, R, Java, C. Candidates must be familiar with the functional genomics experiments indicated above and have experience analyzing data from these experiments using robust computational
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, computer science, or applied mathematics, and superb computer programming skills. Recommended skills include an advanced command of Python and a language in the C family. Ability and a strong desire to work in a
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' datasets - genomics transcriptomics, and proteomics Proficiency in R, Python, and other programming languages Expertise in Linux, Git, Docker, and other high-performance computing environments Excellent
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remote sensing/climate data. You have programming skills in Python and/or R; you are familiar with reproducible coding and automated geospatial data analysis. You have excellent scientific writing and
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. You have good programming skills in Python and/or R; you are familiar with reproducible coding and automated (geospatial) data analysis. You have excellent scientific writing and communication skills in
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(e.g. Matlab / Python) Experience of or willingness to work with pre-clinical tissue models (e.g. farmed meat) and human tissue (e.g. contaminated instruments in clinical facilities). A proven academic
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in R or Python. Experience in applied economic analysis, with preference given to those with experience developing and using structural economic models. Experience with U.S. commodity price analysis
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one of the following programming languages: R, Perl, Python, C/C++, JAVA, etc. Experience in cancer research and/or molecular biology will be an advantage. Preference will be given to those with
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production with an associated photon. • Technical skills in the use of columnar analysis approaches with python. Desired Qualifications • Experience with C++ programming. • Experience in developing
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such as glaucoma, macular degeneration, and uveitis. Programming in Python and R languages with knowledge of Google Tensorflow, PyTorch, scikit-learn, and Keras or other related deep learning libraries