The Mollah lab in the Department of Genetics at Washington University School of Medicine in St.Louis is seeking a highly motivated individual for a postdoctoral research fellow (computational) to join her lab to develop novel computational approaches to understand the molecular mechanisms underlying cancer and other rare diseases. An ideal candidate for this position will possess a strong computational background in machine learning, computer science, mathematics, physics, and related sciences combined with a good understanding of molecular biology, biochemistry, in the area of epigenetics. In addition, the ideal candidate will have some experience and publications in network-based biology and regulatory/signaling networks. Development of integrative network-based models using multi-omics data is the main focus of Mollah’s lab. As a postdoctoral fellow, the candidate will work collaboratively with multidisciplinary teams to develop or  improve algorithms for cancer and translational research within and outside the institution. The research will involve development of network-based models to predict how the genomic and epigenomic factors affect physiologic or pathologic phenotypes, analysis of cell regulatory and signaling networks for elucidating biological mechanisms of diseases at the systems level.

The initial appointment will be for up to one to three years and can be renewed for up to a total of five years, depending on the candidate’s goals and qualifications.

Required Qualifications

• Ph.D. degree in one of the following quantitative disciplines: bioinformatics, computational biology, computer science, mathematics, statistics, genetics/genomics & related engineering fields. Additional work-related experience will be a plus.
• Strong candidates from a primarily wet-lab or clinical background who wish to develop sophisticated quantitative skills will also be considered.

Preferred Qualifications

• Knowledge of computer languages, including R, Python, PERL, UNIX shell scripts, C/C++, and Java.
• Familiarity with processing large genomic and proteomic data sets.
• Track record of scientific productivity, e.g. a first author paper, or a demonstrable contribution to a large project.
• Familiarity with network biology algorithms, as well as with the underlying biological knowledge related to transcriptional and post-translational interactions is highly desired.
• In-depth knowledge of the foundations of linear algebra, machine learning, mathematical modeling, and probability theory.
• Some supervision of trainees.
• Excellent communication and writing skills.

Working Conditions

This position works in a laboratory environment with potential exposure to biological and chemical hazards.  The individual must be physically able to wear protective equipment and to provide standard care to research animals.

Salary Range

Base pay is commensurate with experience.

Applicant Special Instructions

Please send a cover letter, CV, and contact information for three references to Dr. Shamim Mollah at smollah@wustl.edu.