Princeton faculty members can partner with a Research Software Engineer (RSE) through a number of mechanisms that depend on project duration, funding, and faculty affiliation.

Long-term:

These partnerships last more than a year. They are often for long-running projects, entire labs that require dedicated and embedded support for an ongoing, indefinite period of time, or departments / centers / institutes / initiatives that have multiple affiliated projects or labs.

  1. Submitting a proposal at the next Call for Proposals (CFP)
    • If awarded, the position is centrally co-funded, typically at 50% (though not required), via Research Computing. 
    • Calls are made as funding becomes available. The last two CFPs were in 2024 and 2022. Future announcements will be advertised within the Princeton community and posted here.
  2. New partnership for existing RSE
    • Occasionally a long-term partnership comes to an end and the RSE becomes available for a new long-term partnership. Opt-in to the mailing list to learn about these opportunities. These positions are often 50% co-funded by Research Computing.
  3. Full partner funding
    • For faculty who bring 100% of the funding, Research Computing will partner with you to find, hire, and manage the RSE on your behalf.
    • Contact RSE Group Senior Director, Ian Cosden ([email protected]) or any of the RSE Leadership Team to learn more.

Short-term:

These temporary RSE collaborations are often for a single project that is typically less than one year in duration, often 3-9 months. These RSE collaboration avenues typically require no funding from the faculty. Faculty may apply for short-term RSE collaborations through the units listed below. Each unit has its own process for selecting RSE projects:

  1. Molecular Biology: apply in response to MOL faculty mailing list announcement
  2. Humanities: contact the Center for Digital Humanities
  3. Social Sciences: apply through the Initiative for Data-Driven Social Science
  4. Any affiliation: apply through the Center for Statistics and Machine Learning