Postdoc in statistical genomics and systems biology

The Jackson Laboratory for Genomic Medicine
Job Location
Farmington, CT
Job Description

Statistical modeling for (1) lncRNA, (2) 3D genome, or (3) gene regulatory network. Methodology development as well as collaboration with experimentalists on health and disease systems such as cancer, stem cells, immunology, diabetes, infectious diseases, and microbiome.


Ph.D. degree in statistics, biostatistics, computer science, applied mathematics, bioinformatics, or computational biology. Data analysis and programming skills (R/Python/Perl/C/C++) are essential. Prior experience in genomics and next generation sequencing is a strong plus.

How to Apply

Interested applicants should submit a CV and contact information of three references to Prof. Zhengqing Ouyang ([email protected]). Ouyang Lab website:

About Our Organization

The Jackson Laboratory for Genomic Medicine is an academic research institute in the state of Connecticut, representing an expansion of The Jackson Laboratory. The campus is adjacent to University of Connecticut Medical School and in the Hartford metropolitan area. It is within 2 hour drive of both New York City and Boston.

The Jackson Laboratory offers a unique research and training environment characterized by scientific collaboration, unparalleled genomics resources, and outstanding research support services. It is voted among the top 15 "Best Places to Work in Academia" in the United States for 2012 in a poll conducted by The Scientist magazine. Postdoc training at JAX:

New study finds bias against female lecturers among student course evaluations, the Economist reports.

A research duo finds that science and technology graduate students who turn away from academic careers do so because of changes in their own interests.

Students whose classmates are interested in science are more likely to think about a career in science, technology, engineering, and mathematics, a new study says.

CNBC reports that the genetic counseling field is expected to grow as personalized medicine becomes more common.