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the oceanographic community including MatLab, Ferret, and/or Python. ● The incumbent must have a proven publication record, strong scientific writing and computer skills, and the ability to communicate science to
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, Statistics, Genetics, Computer Science, or a related field. Excellent communications skills in English (both verbal and written). Proficiency in using R/Bioconductor and Perl/Python for genomic data analysis
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learning methods Strong programming skills in Python and/or R Experience with deep learning frameworks (PyTorch or TensorFlow) Knowledge, Skills and Attitudes: Snakemake pipeline development with Conda
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communication—not coding or software development. Candidates whose primary experience is in Python, R, SQL, statistical modeling, or data engineering will not be a match for this position. The University of Miami
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especially interested in candidates who can teach a variety of technical class areas of teaching need including PYTHON, Data Base (SQL), Tableau, AWS (Cloud), PM and JAVA at a high level of proficiency
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bioinformatics workflows on large-scale genomic, proteomic, lipidomic, and clinical datasets. Develop, optimize, and maintain reproducible data-processing pipelines using R, Python, Bash, and workflow management
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interfaces for analyzing and manipulating research data applications including expertise with simple programming using Perl/Python or Java, statistical programming, web-development, and database query through
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statistical expertise in data visualization, study design, and simulation studies. Develops R or Python scripts for data analysis, statistical modeling, and machine learning techniques, ensuring reproducibility
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, checking, recoding data for analysis). At least intermediate knowledge of SAS or R programming required. Knowledge of Python and/or machine learning helpful but not required. The University of Miami offers
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cytometry. • Must have experience working with vertebrate animal models. • Experience with CRISPR and shRNA technology preferred. • Experience with R or Python and ability to analyze large datasets