Post Doc Associate

University of Maryland • College Park, MD

4d • $85,000 - $90,000 • Onsite

About The Position

The work will primarily focus on data analyses and visualization. The position consists of a high degree of numerical analysis and computer programming. The applicant will be expected to build and maintain reproducible pipelines for processing metabolomics, bile acid profiles, and metagenomic sequencing data from dietary intervention studies. This includes implementing multi-omic data processing approaches with emphasis on large-scale sequencing data handling and multi-omic integration used to study microbial systems. The role involves applying multi-omics frameworks to integrate metabolomics, bile acids, and metagenomics for understanding microbial function, and developing predictive models in R and Python or related platforms to link dietary exposures to health outcomes. The Post Doc Associate will use tools such as QIIME2, MetaPhlAn, HUMAnN, Kraken2, and R packages like phyloseq and vegan to perform taxonomic and functional profiling. They will apply workflows for diversity analysis, functional gene profiling, and bioinformatic pipeline execution in developing and executing microbial community analysis pipelines. Additionally, the role includes conducting metabolite-microbe association analyses and pathway enrichment using tools such as MetaboAnalystR, mummichog, and KEGG/MetaCyc, building predictive models to identify individual responses to dietary interventions, and creating high-quality visualizations to communicate multi-omics findings. The position also requires preparing figures, reports, and manuscripts that clearly convey complex results to interdisciplinary teams and presenting findings at meetings to nutrition scientists, clinicians, microbiologists, and collaborators.

Requirements

• Ph.D. in computational biology, bioinformatics or a related science field with a strong emphasis on genomic data analysis and data visualization.

Responsibilities

• Build and maintain reproducible pipelines for processing metabolomics, bile acid profiles, and metagenomic sequencing data from dietary intervention studies.
• Implement multi-omic data processing approaches with emphasis on large-scale sequencing data handling and multi-omic integration used to study microbial systems.
• Apply multi-omics frameworks to integrate metabolomics, bile acids, and metagenomics for understanding microbial function.
• Develop predictive models in R and Python or related platforms to link dietary exposures to health outcomes.
• Use tools such as QIIME2, MetaPhlAn, HUMAnN, Kraken2, and R packages like phyloseq and vegan to perform taxonomic and functional profiling.
• Apply workflows for diversity analysis, functional gene profiling, and bioinformatic pipeline execution in developing and executing microbial community analysis pipelines.
• Conduct metabolite-microbe association analyses and pathway enrichment using tools such as MetaboAnalystR, mummichog, and KEGG/MetaCyc.
• Build predictive models to identify individual responses to dietary interventions.
• Create high-quality visualizations to communicate multi-omics findings.
• Prepare figures, reports, and manuscripts that clearly convey complex results to interdisciplinary teams.
• Present findings at meetings to nutrition scientists, clinicians, microbiologists, and collaborators.

Benefits

• For more information on Regular Faculty benefits, select this link.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree