The Greene lab welcomes applications for a computational postdoctoral position at the University of Pennsylvania’s Perelman School of Medicine. The Greene lab's overarching goal is to transform how we understand complex biological systems by developing and applying computational algorithms that effectively model processes by integrating multiple types of big data from diverse experiments. The successful candidate will have the opportunity to work with large collections of microbial transcriptomic data, both publicly available and from collaborative projects.


This position will focus on the development and rigorous evaluation of new models for microbial transcriptomes and the development of methods to predict the genome-wide outcome of microbial responses to perturbations. We will extract testable biological hypotheses from these large collections and test these hypotheses in collaboration with the Hogan Lab at The Geisel School of Medicine at Dartmouth. Postdocs are expected to contribute to the lab's culture of open scientific discovery and to share methodological advances and biological discoveries at both national and international venues.


  1. Candidates are expected to have an MD, PhD, or equivalent, with programming experience and a strong background in microbiology, genetics, machine learning, statistics, bioinformatics, or closely related field.
  2. Candidates are expected to strive for robust and reproducible analytical workflows.

  3. The ideal candidate will also have attributable contributions to publicly available source code.

  4. The ideal candidate will have experience handling large datasets in a UNIX/LINUX environment, experience with high performance cluster or cloud computing, and a knowledge of existing software packages used for machine learning.

The Greene Lab focuses on open, reproducible science. Software and analytical workflows produced by the lab are available primarily via GitHub ( A listing of recent publications is available on the lab website ( We aim for a diverse group of lab members with complementary expertise and experience ( Our lab procedures are available on GitHub (

About the lab


To apply, send a cover letter that includes the names and contact information for three references, a short statement of research interests, and a current CV to Casey Greene (