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Field
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-validation by droplet digital and real-time PCR. Contribute to the host–microbiome analyses (GWAS/mGWAS, metagenomics) and the integrative modeling led by the graduate student. Apply machine-learning and AI
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learning methods to analyze high-dimensional multi-omics data, including exposomics, genomics, shotgun metagenomics, glycomics, proteomics, and metabolomics, in the context of human health and disease
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the project based on earlier experience and skills. We work on a range of topics with a focus on high-throughput omics data integration and analysis in population cohort studies (e.g. metagenomics, metabolomics
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adapted and revertant strains through allelic exchange, and examining the contribution of these genetic changes to pathogenesis-related phenotypes. Generating metagenome assembled genomes from shotgun
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Qualifications: Experience analyzing oral microbiome datasets (16S, shotgun metagenomics, or metatranscriptomics). Advanced programming and data science skills in R, Python, or other relevant languages
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endophytic communities • Stress biology; soil and rhizosphere microbiology • Functional microbiology • Next-generation sequencing, metagenomics, RNA-seq and transcriptomics analysis • Multi-omics integration
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sequencing data analysis (shotgun metagenomic, and/or metatranscriptomic), microbial metabolomics, or microbial genetics. Experience designing and conducting experiments that probe the relationship between
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community analysis, or microbial metabolism. Demonstrated expertise in one or more of the following: microbiome sequencing data analysis (shotgun metagenomic, and/or metatranscriptomic), microbial