The University of Arizona

Limited Submissions

What is a Limited Submission?

Limited submissions are funding opportunities where the funder has limited the number of applications from an organization. Research Development Services (RDS) facilitates limited submissions for federal and foundation opportunities for UA. Selected proposals will need to follow UA proposal submission procedures. Please note that proposals to private foundations may require submission and coordination through the University of Arizona Foundation.

The Limited Submissions Newsletter is sent to subscribers on Thursdays and lists all current Upcoming and Open Limited Submissions available to the UA campus. Subscribe here

Limited Submission Preferred Timeline

We strive to notify campus of internal competitions for limited submissions at least 12 weeks prior to the funder’s deadline. Applicants have two weeks to submit an internal pre-proposal to RDS and another 1-2 weeks to receive the go-ahead to submit or not. This timeline allows 8-9 weeks for applicants to prepare their proposal for submission to the funder.

Limited Submission Table

Last updated on February 24, 2020

  • The table below defaults to sorting by the RDI deadline but is also sortable by “Program Title,” or “Sponsor.” Simply click on the title in the table’s header.
  • Unless otherwise noted, internal pre-proposals are due by 5:00p AZ on the RDI deadline date and must be submitted through UA Competition Space.
  • If you want to apply for a limited submission opportunity that is not listed below, or if you have questions about the competitions listed here, please contact

Download limited submissions as a CSV file: Upcoming | Open | Completed


Note: partial words will not return results
Program Titlesort descending Sponsor Research Category Funding Type RDI Deadline External Deadline Notes
2020 January Medical Scientist Training Program (T32) National Institute of General Medical Sciences (NIGMS) Biomedical, Clinical & Life Sciences 11/27/2019



ADVANCE: Increasing the Participation and Advancement of Women in Academic Science and Engineering Careers (ADVANCE) National Science Foundation (NSF) STEM, Education, Training 09/12/2018


UA is ineligible to apply to the Adaptation and Institutional Transformation tracks due to previous grant funding under this program.

Claude D. Pepper Older Americans Independence Centers (P30 Clinical Trial Optional) National Institute of Health Biomedical, Clinical & Life Sciences 06/19/2019


J. Nikolich

CLIR Recordings at Risk Seventh Call The Council on Library and Information Resources Arts & Humanities 11/27/2019



Consortium for Advanced Manufacturing Foresights: Defining the Critical Needs of the Advanced Manufacturing Research Community NSF Physical Sciences & Engineering Research 07/20/2015


T. Koch

Critical-Zone Collaborative Network National Science Foundation (NSF) Environmental Sciences, Physical Sciences & Engineering 10/02/2019


J. Hu
J. McIntosh
C. Rasmussen

Cybersecurity Innovation for Cyberinfrastructure (CICI) NSF Mathematics, Computational, & Data Sciences Research 03/01/2017


K. Melde

Maximizing Access to Research Careers Undergraduate - Student Training in Academic Research (MARC U-STAR) (T34) National Institute of General Medical Sciences (NIGMS STEM, Education, Training 02/28/2018


UA is ineligible to apply due to an existing MARC U-STAR grant award.

National Science Foundation Research Traineeship (NRT) Program National Science Foundation (NSF) STEM, Education, Training 09/12/2018

12/06/2018 (required LOI)
02/06/2019 (full proposal)

B. LeRoy
S. Saleska

NCI Pathway to Independence Award for Outstanding Early Stage Postdoctoral Researchers (K99/R00 - Independent Clinical Trial Not Allowed) National Cancer Institute Biomedical, Clinical & Life Sciences 12/18/2019


UA may submit a combined total of three applications to RFA-CA-20-014 and/or RFA-CA-20-015, and only one per scientific area. Please see the solicitation(s) for full eligibility and limitations.

For RFA-CA-20-014: OPEN

For RFA-CA-20-014: E. Alizadeh for (A) Data Science