Integrative genomics shouldn't be hard!
Our lab is made up of a highly engaged and collaborative team of scientists in the Perelman School of Medicine at the University of Pennsylvania dedicated to: 1) developing methods that analyze big data to understand complex biological systems and 2) putting these methods and big data into the hands of every biologist. We are currently adding highly motivated programmers/software engineers, postdocs, graduate students, and undergraduate students to our team. Read through the appropriate heading below for information about joining the lab.
Programmer & Software Engineer Positions
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 development of usable webservers, the dissemination of successful applications, and the distribution of open source software.
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.
- 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.
- 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.
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.
We welcome graduate students in the Penn Biomedical Graduate Studies program to rotate in the lab. Our lab is currently affiliated with the Genomics and Computational Biology (GCB) program and the Cell and Molecular Biology (CAMB) program through the Genetics and Gene Regulation (GGR) graduate group. 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 e-mail Casey to set up a time to discuss your interest.
We welcome undergraduate students into our lab, and 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. As an undergraduate, if you are interested in discussing research opportunities please e-mail Casey.