Join our Team
See below for the current job openings in the Khalili Laboratory.
The Massachusetts General Hospital (MGH), a Harvard affiliate, is looking for exceptional candidates to join the research group of Dr. Hamed Khalili in the Clinical and Translational Epidemiology Unit (CTEU) at Mongan Institute. Successful candidates will join a research program focused on population-based and translational research efforts in gastrointestinal diseases. We seek to understand the lifestyle and environmental risk factors for chronic gastrointestinal diseases such as inflammatory bowel diseases.
Postdoctoral Research Fellow
This Biostatistician Epidemiologist Postdoctoral Research Fellow position offers the opportunity to work directly with a team of Principal Investigators (PIs) in the CTEU/Mongan Institute and the Division of Gastroenterology at MGH. Responsibilities will include developing research protocols, compiling data, conducting statistical analyses, validating programming and reporting output in numerical and graphic formats under the supervision of the PI. In addition to traditional epidemiologic studies, the candidate will work with a variety of omics data (e.g. metagenome, metabolomics, etc.) The candidate will need a thorough understanding of data/information quality principles as well as be comfortable with data manipulation in large databases. Candidate must be self-motivated, independent in task completion, and comfortable accepting feedback and guidance from colleagues. The ability to work well in a team is a must, as well as the maturity to manage numerous research studies simultaneously.
Clinical Research Coordinator
We are seeking a Clinical Research Coordinator to work on the NIH Nutrition for Precision Health (NPH) study. NPH aims to enroll a cohort of individuals from the All of Us Research Program that is heterogeneous in demographics, clinical characteristics, health parameters and disease risk factors to inform more personalized nutrition recommendations. NPH will collect new data on multiple potential predictive factors and combine it with existing data in the All of Us database to develop a more complete picture of how individuals respond to different foods or dietary routines. Ultimately, we will use this research to develop algorithms to predict individual responses to food and dietary routines.