Postdoctoral Fellow-MSH-76700-134

Position Title: Postdoctoral Fellow

Department: Environmental Medicine 

Institution: Icahn School of Medicine at Mount Sinai 

Location: New York, NY 

Employment Type: Full Time 

Start Date: Summer/Fall 2026

About

The Department of Environmental Medicine at the Icahn School of Medicine at Mount Sinai seeks a highly motivated and talented postdoctoral research fellow with interests in environmental health and data science. 

The candidate will join a team that leverages AI to phenotype pathway-specific strokes in electronic health records and investigates the effects of multiple environmental and built environment exposures on stroke risk. This position is well-suited for a candidate with a strong background in quantitative analysis and epidemiology seeking experience in the application of AI in environmental health research.

The Department offers opportunities for career advancement, including support for grant applications to fund career independence and the opportunity to attend workshops and both national and international conferences. This position offers a substantial compensation package, a tailored benefits package, and generous paid time off. Postdoctoral fellows are also eligible to apply for subsidized housing in Manhattan. 

• Carry out large-scale statistical analyses 

• Apply novel statistical approaches 

• Collaborate with scientists with different backgrounds

• Write scientific manuscripts

A doctoral degree (e.g., PhD, MD) is required. Additional required qualifications include:

• PhD in epidemiology, biostatistics, or related fields

• Advanced knowledge and programming skills in R

• Excellent analytical and computational skills 

• Ability to work on concurrent projects

• Ability to work independently as well as part of a team

• Strong organizational and time management skills

• Strong work ethic and the ability to meet deadlines

Preferred Qualifications

• Advanced knowledge of Python

• Experience in the application of Large Language Models (LLM) 

• Experience with advanced statistical methods used in environmental health

• Experience analyzing spatial data

• Expertise in tools that promote reproducible research (Git, etc.)

• Good communication and writing skills