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Computational Research in Network Biology of Gene Regulation and Diseases

National Library of Medicine, Bethesda, MD and surrounding area

Position Description:

The Computational Biology Branch (CBB) of the National Center for Biotechnology Information (NCBI) at the National Library of Medicine (NLM) is looking for a qualified postdoctoral fellow to perform data-driven research in network biology of gene regulation and disease. NLM is one of 27 Institutes at the National Institutes of Health (NIH), part of the Department of Health and Human Services (DHHS).

Research at CBB focuses on theoretical, analytical, and applied computational approaches to a broad range of fundamental problems in molecular biology and medicine. The expertise of Dr. Teresa Przytycka’s research group ( concentrates in data analysis, network biology, and methods for analysis of new emerging data types. In addition, research interests cover a wider range of topics in computational biology. There is particular interest in the application of algorithmic and machine learning methods to these areas. The successful applicant will be given flexibility to pursue research in broader applications in these areas to computational biology and biomedicine.


Appointees may be U.S. citizens, permanent residents, or foreign nationals (visa requirements apply). Candidates are subject to a background investigation.

Applicants must have a doctorate degree in computer science, bioinformatics, or a similar quantitative discipline. Individuals with good programming skills, familiarity with algorithmic techniques data are encouraged to apply. Preference will be given to candidates with prior experience in analysis of biological data .

To Apply:

Applicants should send a cover letter, curriculum vitae, and contact information of three references (names and e-mail addresses) to Dr. Teresa Przytycka. For more information about this position, please contact her at Applications will be accepted until the position is filled.

The NIH is dedicated to building a community in its training and employment programs and encourages the application and nomination of qualified women, minorities, and individuals with disabilities.