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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 1 month ago
knowledge of the molecular dynamics simulations, polymer physical chemistry, and data analysis, Linux operating systems, programming skills and scripting, all of which could be obtained during
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are both essential. Experience working in a high-performance Linux cluster computing environment is also desirable. Applications must be submitted online and should include a cover letter and a curriculum
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. Preferred Experience in genomic data analysis, computational biology, statistical genetics, or functional genomics. Proficiency in R, Python, Linux/Unix, and high-performance computing environments
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-environment association analyses, and expertise using and trouble-shooting bioinformatic tools, as well as experience using Unix or Linux environments is required. The postdoctoral scholar will be expected
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Computational Postdoctoral Researcher - Bacterial Genomics, Transcriptomics, and Single-Cell Genomic
. • Strong computational and programming skills (Python, R, Linux, and high-performance computing environments). • Demonstrated experience in genomics data analysis, evidenced by peer-reviewed publications
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experience in statistical genetics or genomics research, demonstrated through a peer-reviewed publication record, and excellent programming skills (Python, R, Linux, GitHub). Experience wrangling and analysing
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human omics (preferably related to neurodegeneration) studies is expected. Solid skills in at least one programming language (R, Python or Perl) and experience working in Linux and/or high-performance
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models). Climate emulators for wave and sea level simulations. Use of Linux-based numerical computing servers. Specific Requirements Knowledge: Coastal morphodynamic processes at different time scales
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applied to genome and/or transcriptome analysis; iii) Familiarity with Linux environments, command-line tools, and the execution of computational pipelines; iv) Experience in at least some of the following
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advanced AI frameworks (TensorFlow, PyTorch, Scikit-learn). Experience with bioinformatics tools and databases (e.g., Bioconductor, Galaxy, KEGG, Reactome, STRING). Proficiency in Python, R, and Unix/Linux