Data Scientist / Computational Postdoctoral Fellow

Organization
Charles Bronfman Institute for Personalized Medicine, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai
Job Location
1468 Madison Avenue
New York, NY 10037
Benefits

Information on the Postdoctoral Training Program at Mount Sinai:  http://icahn.mssm.edu/education/postdoctoral-training

The lab is based at the main campus, which stretches from East 98th to 102nd Streets between Madison and Fifth Avenues on Manhattan's Upper East Side and Central Park.

Incoming postdoctoral fellows are eligible for affordable Mount Sinai Housing within walking distance of the medical school and of a wide range of amenities.

Job Description

A computational data scientist or computational postdoc position is available immediately in Dr. Ron Do’s lab. The Do lab is in the Charles Bronfman Institute for Personalized Medicine, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York.

The Charles Bronfman Institute for Personalized Medicine is an interdisciplinary institute to advance personalized health and health care. One of the institute’s key resources is the BioMe electronic health record (EHR)-linked Biobank, an ancestrally diverse population of >34,000 individuals recruited from throughout New York City. BioMe has a longitudinal design and captures and full spectrum of common and rare biomedical phenotypes. BioMe is also rich in genetic data, including genome-wide array genotypes, whole exome sequencing and whole genome sequencing data.

Dr. Do’s lab focuses on determining the genetic and biological bases of complex disease. The group pursues these interests by utilizing approaches from statistical genetics, population genetics, human genetics and genetic epidemiology.

Current lab research areas include: (1) Causal inference of biomarkers with complex disease; (2) Identification of biological processes of complex disease using functional data; (3) Inferring the strength and mode of natural selection for complex disease; (4) Rare variant association studies using sequencing data; (5) Data mining in electronic health records.

Lab members will benefit from collaborations with neighboring labs in the Charles Bronfman Institute for Personalized Medicine, the Center for Statistical Genetics, and the Icahn Institute for Genomics and Multiscale Biology.

The term for this position is for 2 years with possibility of an extension depending on successful progress and available funding. A competitive salary, benefits and travel opportunities will be offered commensurate with experience and qualifications.

Requirements

1. Candidates should have a Ph.D., M.D. or equivalent doctorate in Human Genetics, Statistical Genetics, Population Genetics, Statistics, Bioinformatics, or a related discipline.

2. Candidates should have proficiency in programming (e.g. Perl or Python) and statistical computing (e.g. R).

3. Candidates should have a track record of scientific productivity and/or leadership.

How to Apply

Please send inquiries via email to ron.do(at)mssm.edu. Informal inquiries are welcome.

Information on the Postdoctoral Training Program at Mount Sinai: http://icahn.mssm.edu/education/postdoctoral-training

Information on our program: http://labs.icahn.mssm.edu/dolab/

About Our Organization

The Icahn School of Medicine at Mount Sinai is internationally recognized as a leader in groundbreaking clinical and basic science research and is known for its innovative approach to medical education.

With a faculty of more than 3,400 in 38 clinical and basic science departments and centers, Mount Sinai ranks among the top 20 medical schools in receipt of National Institutes of Health grants. In its 2012 "America’s Best Graduate Schools" issue, U.S. News & World Report ranks the Icahn School of Medicine 18th out of 126 medical schools nationwide.

Mount Sinai Medical Center is an equal opportunity/affirmative action employer. We recognize the power and importance of a diverse employee population and strongly encourage applicants with various experiences and backgrounds.

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