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, design and analysis of virus-derived RNA libraries, and development of machine learning models for detecting functional elements in viral metagenomic datasets. This project is a collaboration with the
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to the discovery of dozens of classes of bacterial riboswitches and other structured noncoding RNAs. We are now applying similar genome-scale discovery methods to identify functional RNA regulatory elements in
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well as part of an ENCODE functional characterization center. The lab is especially interested in non-coding cis-regulatory elements (CREs) and the variation within them, using high-throughput experimental
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research Strong quantitative skills, including experience with regression, structural equation modeling (SEM), and multilevel modeling (MLM) Experience with measurement development, factor analytic methods
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) consortium as well as part of an ENCODE functional characterization center. The lab is especially interested in non-coding cis-regulatory elements (CREs) and the variation within them, using high-throughput