Postdoctoral Fellow-MSH

Roles & Responsibilities:

Postdoctoral Fellowship in Exposomics, Environmental Epidemiology, Environmental Medicine, Public Health, Biostatistics, Pediatrics and Medicine

A fully-funded PostDoc position is available with Dr. Alison Lee at the Icahn School of Medicine at Mount Sinai in NYC. Funding is guaranteed for 2 years with potential for further support. Dr. Lee’s research leverages two NIH-funded ongoing pregnancy cohort studies investigating the impact of perinatal exposures (both chemical and non-chemical) on lung development and the programming of future respiratory disease in children, with a specific focus on epigenetic mechanisms including DNA methylation, non-coding microRNAs and long noncoding RNAs, and extracellular vesicle communications. Metabolomics and microbiome are also of interest. Further, these studies are part of the national ECHO (Environmental Influences on Children’s Health Outcomes) Initiative and consider a range of additional outcomes, including growth and obesity, perinatal outcomes and neuropsychological development, allowing extension of epigenetic work to these outcomes. The successful postdoc will lead epidemiology and computational investigations of how these molecular mechanisms mediate the effects of high-resolution environmental exposures on human health.

Examples of active projects include:

1. Maternal trauma, circulating microRNA in extracellular vesicles, and programming of childhood respiratory outcomes

2. Prenatal PM2.5 and programming of respiratory outcomes: Placental biomarkers and effect modification by stress

3. ECHO consortium on perinatal programming of neurodevelopment

Team: The fellow will work closely with Dr. Lee and will be part of an interdisciplinary team of environmental epidemiologists, epigeneticists, and computational biologists dedicated to environmental health investigations.
 

Requirements:

Qualifications: Applicants should possess a Ph.D. in Environmental and/or Molecular Epidemiology, or alternatively, in other relevant disciplines, such as Statistics/Biostatistics, Computational Biology, Machine Learning, or Bioinformatics, with relevant previous work and interest in environmental health. Strong quantitative skills, practical experience working with complex epidemiology data, familiarity with the R statistical software packages and excellent oral communication and scientific writing are also necessary. Experience with high dimensional molecular data such as Illumina BeadChip methylation (450K, 850K or EPIC) and RNA-seq data is strongly preferred.

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