Postdoctoral Fellow – Epidemiology & Urogenital Microbiome

Postdoctoral Fellow – Epidemiology & Urogenital Microbiome

University of Maryland School of Medicine
Institute for Genome Sciences (IGS)
Center for Advanced Microbiome Research and Innovation (CAMRI)

The Brotman Laboratory is seeking a Postdoctoral Fellow to contribute to high-impact research at the intersection of epidemiology, microbiome science, and women's health.

Our work focuses on understanding how the urogenital microbiome influences susceptibility to sexually transmitted infections (STIs), bacterial vaginosis (BV), urinary tract infections (UTIs), and genitourinary syndrome of menopause (GSM). We leverage large, deeply characterized longitudinal cohorts and biorepositories, integrating metagenomics, transcriptomics, metabolomics, and host immune profiling.

Responsibilities

• Lead epidemiologic and statistical analyses of longitudinal and high-dimensional microbiome datasets
• Apply and develop methods for multi-omic data integration (e.g., metagenomics, metabolomics, transcriptomics, immune profiling)
• Interpret findings within an epidemiological and biological framework
• Lead and contribute to manuscript development for high-impact peer-reviewed journals
• Present findings at national and international scientific meetings
• Collaborate closely with statisticians, bioinformaticians, and laboratory scientists
• Contribute to study design, analytical plans, and grant development

Qualifications

• PhD, ScD, or equivalent doctoral degree in epidemiology, biostatistics, bioinformatics, microbiology, or a related field
• Strong quantitative and statistical skills, with proficiency in R or Python
• Experience working with longitudinal data, infectious disease epidemiology, or high-dimensional datasets
• Demonstrated track record of peer-reviewed publications
• Strong writing skills and ability to translate complex analyses into clear scientific narratives
• Interest in microbiome science and women's health
• Preferred but not required: Experience with microbiome or multi-omic data analysis, causal inference methods, or infectious disease epidemiology