Postdoctoral Researcher at Greene Lab

Greene Lab

Postdoctoral Researcher

Full-Time in Philadelphia, PA

The Greene lab welcomes applications for computational postdoctoral positions at the University of Pennsylvania 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. Our goal is to then put these algorithms into the hands of biologists through the dissemination of successful workflows and open source software to carry out those workflows.

Postdocs in our lab contribute to the development of new analytical methods and/or the application of these methods to new datasets. Postdocs will have the opportunity to work with large collections of genomic data, both publicly available and from collaborative projects, and to extract testable biological hypotheses from these large collections.

Applicants will be asked to select from one of two tracks for postdoctoral positions, which have somewhat different objectives and qualifications.

COMPUTATIONAL BIOLOGY POSTDOC OBJECTIVES:

Research projects center around new computational methods to integrate genomic data as well as to incorporate additional environmental and phenotypic information with these genomic data. 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. The goal of this position is to develop the expertise necessary for the trainee to lead a group taking on independent research challenges in broad areas of computational biology.

COMPUTATIONAL BIOLOGY QUALIFICATIONS:

  • Candidates are expected to have a PhD, MD or equivalent doctoral degree at the time the position would start, with a strong background in computer science, machine learning, statistics, genetics, bioinformatics, or closely related field and programming experience with attributable contributions to source code.
  • The ideal candidate will have a track record of scientific productivity and leadership and will strive for robust and reproducible analytical workflows.
  • 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.

CHILDHOOD CANCER-FOCUSED POSTDOC OBJECTIVES:

Research projects for these postdocs will center around the use of data to find new treatments and cures for childhood cancers: either through the development of new methods or the application of existing methods. Postdocs in this group will be physically located at the Childhood Cancer Data Lab, an initiative of Alex's Lemonade Stand Foundation, which is located in Center City Philadelphia, and they will hold a postdoctoral position at Penn, with full access to Penn biomedical postdoc programs.

This is a unique opportunity to be part of a team of designers, software engineers, and biological data scientists at the first informatics lab dedicated to childhood cancer housed at a funding organization. The ideal candidate will be interested in obtaining an independent investigator position focused on data-intensive pediatric cancer research within 3 years of starting the position.

CHILDHOOD CANCER-FOCUSED QUALIFICATIONS:

  1. Candidates are expected to have an PhD, MD, or equivalent degree with either
    1. A strong background in computer science, machine learning, statistics, genetics, bioinformatics, or closely related field and programming experience with attributable contributions to source code and a primary interest in applying these skills to pediatric cancers
    2. Deep expertise in pediatric cancer with some experience in biological data science and a willingness to grow their skill set in this domain.
  2. The ideal candidate will have a track record of scientific productivity and leadership and will strive for robust and reproducible analytical workflows.