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analysis, expertise in multi-omics data integration, and working experience with computational modeling and machine learning. The ideal candidate will be able to process and analyze high dimensional
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applied to environmental chemical exposures, type 2 diabetes, kidney disease, liver cancer, and renal cancer. The ideal candidate will be open-minded, eager to learn, and enthusiastic about engaging in our
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modeling and machine learning. The ideal candidate will work independently and will be able to process and analyze high dimensional omics data (genomic, epigenomic, transcriptomic and metabolomic
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of genomic, epigenomic, and transcriptomic data processing and analysis, expertise in multi-omics data integration, and working experience with computational modeling and machine learning. The ideal candidate
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integration, and working knowledge of computational modeling and machine learning. The ideal candidate will be able to analyze high dimensional sequencing data, perform network-based analysis (e.g. , WGCNA) and
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of gerontology: https://gero.usc.edu The Irimia Laboratory leverages neuroimaging, neurogenomics, and deep learning to study the aging brain in health and disease, particularly in neurodegenerative conditions like