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, integration, and analysis of large, diverse datasets that benefit from high performance computing (HPC) clusters. The objective of this fellowship is to facilitate cross-disciplinary, cross-location research
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sequence data analysis Comfort with BASH, R, and command-line usage on an HPC environments Comfort with Python3, Git, and workflow management Experience in entomological systems Point of Contact Janeen
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and Geographic Information Systems (GIS) - Computer science and informatics, including HPC, cloud computing, and data architecture Point of Contact Mikeala Eligibility Requirements Degree: Doctoral
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of large-scale genomic and transcriptomic datasets ('big data'), with hands-on experience in high-performance computing (HPC) environments (e.g., command-line interface, scripting in R/Python, use of common
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bioinformatics tools, pipelines, and statistical methods for the analysis of large-scale genomic and transcriptomic datasets ('big data'), with hands-on experience in high-performance computing (HPC) environments
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magnesiothermic reduction. High performance computing (HPC) such as density functional theory (DFT) and grand canonical Monte Carlo (GCMC) simulations calculations will be performed at National Energy Technology