Postdoctoral position - Microbial omics data analysis

Project and missions

Project Background

Microbial ecosystems are highly dynamic and shaped by ecological principles. Advances in high-throughput sequencing and omics technologies have significantly enhanced our ability to study microbial communities at multiple levels, including taxonomy, function, and metabolic activity.

However, current computational methods often treat omics data as purely numerical, overlooking the ecological context that drives microbial community assembly and function. This project aims to develop innovative statistical approaches that integrate ecological principles into omics data analysis, improving our understanding of microbial interactions and microbial ecosystem functioning.

Mission and activities

The postdoctoral researcher will develop new statistical approaches for analyzing microbial omics data that incorporate ecological knowledge. The main objectives of the research will be:

• To evaluate and benchmark existing ecological distance metrics and multivariate statistical approaches for microbial omics data analysis (e.g., metagenomics, metabolomics, transcriptomics).
• To develop novel or refined methods that integrate ecological knowledge into omics data analysis, providing a new dissimilarity index for microbial omics data analysis, enhancing the biological interpretability of results.
• To implement, validate, and apply these methods to existing microbial (multi)-omics datasets.

To contribute to scientific publications, and present research finding at international conferences.

Desired qualifications

• PhD in statistics, or in microbial ecology, or a related field. The position is open only to candidates who have defended their PhD no more than three years before the start date of the postdoctoral contract
• Strong proficiency in R programming
• Experience in data analysis 
• Familiarity with microbial omics data analysis (e.g., metagenomics, metabolomics, metatranscriptomics) or ecological data analysis is a plus
• Demonstrated ability in scientific writing 
• Interest in interdisciplinary collaboration