Postdoctoral fellow in bioinformatics

Indiana University School of Medicine
​Indianapolis, Indiana, United States
Job Type:
  • Postdoctoral
Degree Level Required:
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Postdoctoral fellow in bioinformatics

Dr. Lei Yang’s lab at Indiana University School of Medicine focuses on the mechanisms study of early human heart formation and human inherited cardiovascular diseases using human embryonic stem (ES) cell, patient-derived induced pluripotent stem (iPS) cell and mouse genetic models. We have well-established platform for performing cellular and molecular studies with multiple publications (Developmental Cell. 2017. 42: 333-348; Nature Communications. 2013. 4; Nature. 2008, 453: 524-528).

A postdoctoral fellow position is now available for joint research labs of Dr. Lei Yang and Dr. Jun Wan. Dr. Jun Wan is director of Collaborative Core for Cancer Bioinformatics (C3B) shared by Indiana University Simon Comprehensive Cancer Center and Purdue University Center for Cancer Research. He has broad interests in using cutting edge technologies and approaches to study gene regulatory network, particularly to explore master regulators for gene programming and understand mechanisms of gene regulation. Their work have been published in high impact journals, e.g. Nature Nanotechnology, Cell, Molecular Cell, Journal of Clinical Investigation, PNAS, Nature Communications, eLife, Nucleic Acid Research.

The IU School of Medicine is the largest medical school in the United State and ranks top 33 NIH-funded medical schools in the nation. Compensation is at NIH scale. The successful candidate will have access to the generous retirement benefits package in Indiana University.


The successful candidate will have a unique multi-disciplinary collaboration opportunity to work with interdisciplinary teams of scientists including basic research and computational sciences. He/She is expected to use and/or develop novel computational approaches to analyze data of next generation sequencing (NGS) to support studies of early stage human heart development, disease and regeneration.


A Ph.D. degree in Computer Science, Bioinformatics, Biostatistics, Biology, or a related field is required. The ideal candidate should have experience in NGS data analysis and be proficient in programming languages including R and Python.