Join the Team

Programmers

Our programmers contribute most of the code that goes into our production-ready servers, apps, and analytical pipelines. They are expected to maintain high standards of documentation, clarity, efficiency, and maintainability in their own code, and also help the other members of the lab achieve the same in their code.

For the machine learning aspects of our work, we currently use a technology stack consisting of Python, numpy/scipy/scikit-learn/theano, and R with assorted packages. For web development, we currently use a technology stack consisting of Postgres, Python, Django, JavaScript, and React. Our data processing framework and webservers run on cloud instances, so experience with optimizing workloads for this type of environment is a plus.

Contact Casey to be notified when job postings are available.

Postdoctoral Researchers

The Greene Lab welcomes applications for computational postdoctoral positions at the University of Pennsylvania Perelman School of Medicine. The overarching theme of our work is to transform how we understand complex biological systems by integrating big data from diverse sources and developing computational methods that effectively model real-world processes. We then put these methods into the hands of every biologist, through the development of usable open source tools, and the dissemination of successful applications.

Objectives

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. 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.

Qualifications

  1. Candidates are expected to have an MD, PhD, or equivalent, 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.

  2. The ideal candidate will have a track record of scientific productivity and leadership, and will strive for robust and reproducible analytical workflows.

  3. 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.

Pay and Benefits

Postdoc positions in our lab start at a non-negotiable starting salary, which is currently $70,000 per year. These positions are considered Postdoctoral Trainee Appointments at Penn, which makes the postdoc eligible for an insurance plan and other benefits, including the New Child Leave Policy. Additional information about Biomedical Postdoc Positions at Penn is available on the program website.

Applying

To apply, provide a CV, a one to two page statement of research interests, and the names and contact information for three references through our application portal.

Graduate Students

We welcome graduate students in Penn’s Genomics and Computational Biology (GCB) program. Our goal is to help our students to develop both a deep familiarity with the computational methods required for data-intensive science and a strong understanding of one or more biological application areas. We provide training in all aspects through group meetings, individual meetings, and a supportive lab environment.

If you are interested in rotating in our lab, please contact Casey to set up a time to discuss your interest.

Undergraduate Students

We welcome undergraduate students into our lab. Undergraduate researchers have become first authors on papers submitted on research that they performed as part of our group. We are happy to train undergraduates in many aspects of data-intensive biology, and we have high expectations for their level of commitment to research.

If you are an undergraduate interested in discussing research opportunities please contact Casey.