Postdoc in Human Genetics

The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai
Job Location
New York, NY 10029

A competitive salary, benefits and travel opportunities will be offered commensurate with experience and qualifications.

Job Description

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


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


Current lab research areas include: (1) Causal inference of biomarkers with complex disease (2) Rare variant association studies using sequencing data (3) Identification of biological processes of complex disease using functional data (4) Testing for natural selection in medically relevant loci.


The successful candidate will have the opportunity to work on large-scale cutting-edge sequencing, genotyping and functional data. The candidate will have substantial input to the specific nature of their research project but should broadly fit within the lab’s overall research goals.


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 flexible and can be  up to 3 years. The position may be continued contingent on successful progress and available funding. 


1. Candidates should have a Ph.D., M.D. or equivalent doctorate in human genetics, genetic epidemiology, bioinformatics, computational biology, statistical genetics, 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.

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