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expertise in Systems Biology and Metabolism. Proven experience in analysing large-scale omics datasets (metabolomics, transcriptomics, and metagenomics) and their multimodal data integration. Advanced
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metagenomics from various field experiments, evaluation of microbially mediated regulation of soil carbon fractions, and development of a model that simulates the growth of key soil microbial communities related
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
, Competencies, and Experience 1 year of experience in computational genomics and molecular diagnostics. Microbiome/metagenomic sequencing analysis experience is a plus. Special Physical/Mental Requirements
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University of Lausanne. The ideal candidate will have a microbial ecology and genomics background and experience with metagenomics. Other essential qualifications are a background in statistical methods, a
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integrating biochemical and molecular soil parameters (with a focus on microbiome features from metabarcoding and metagenomics) and agronomic traits generated in horticultural field trials testing organic
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systems, surface/material models, clinically relevant inocula, synthetic communities) and the integration of microbiome omics (16S/shotgun metagenomics and functional profiling) to mechanistically study
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technologies, such as Organ-on-a-Chip development, organoid culture, microbiome sequencing, single-cell transcriptomics, metagenomics and proteomics. This position also leverages collaboration between clinicians
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modeling and computational approaches for bioactive compound analysis. •Next-generation sequencing, metagenomics, RNA-seq and transcriptomics analysis. •Multi-omics integration. •Bioinformatics and data
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in the lab include: • mouse handling • microbial metagenomic sequencing (including whole genome and RNA sequencing) • basic microbiology techniques • molecular cloning • flow cytometry
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biological systems, inflammatory diseases, or diverse clinical patient sample data. Familiarity with processing and analyzing multi-omics datasets (e.g., metagenomics, metabolomics or proteomics) integrated