2026 quarterly application deadlines:

Monday, January 12, 2026
Monday, April 13, 2026
Monday, July 13, 2026
Monday, October 12, 2026

The University of Minnesota Research Computing (RC) Seed Grant program is designed to provide timely, strategic funding to UMN faculty, staff, and students to fund proposals that align with RC’s mission. The goal is to initiate research projects that require access to advanced computing resources and expertise, thereby accelerating discovery and increasing the likelihood of securing larger external grants to grow and sustain this research. This program is a critical component of RC’s mission to enable and sustain cutting-edge research at the University.

Proposals can be divided into the following broad categories:

• Seed funding - the funds will be used towards the preparatory work necessary to submit a larger proposal where RC is a necessary component of the proposal
• Gap funding - the funds are used to complete work that would utilize RC resources and otherwise be impossible to fund, with the goal of, e.g., finalizing the analysis before publication
• Pivot funding - the funds are used to prepare to move to an entirely new direction of research, where RC is a critical provider of resources and expertise
• Shared resource - the funds are used to create a shared resource such as a dataset or repository which requires significant storage, compute or expertise that RC can provide

If the proposal does not fall into any of these categories, then it is not within the scope of this funding mechanism.

Request for Proposals (RFP)

1. Eligibility and Application Process

• This RFP is open to all tenure track faculty at the University of Minnesota who require computational resources to advance their work.
• Applications may be submitted at any time by emailing [email protected] with subject line "RC Seed Grant Application for [PI Name]".
• Proposals will be reviewed on a quarterly basis.

2. Proposal Guidelines

All proposals must adhere to the following financial and scope requirements:

• Maximum Budget: The total budget requested cannot exceed $10,000.
• Budgetary Restrictions: A maximum of 15% of the total budget is permitted for expenses that are not directly offered as a service by Research Computing (e.g., software licenses, data acquisition fees, travel). A minimum of 85% of the budget must be allocated to Research Computing services such as staff consultation, computing time, or data storage.

3. Proposal Components

Each proposal must be submitted as a single document (maximum of 3 pages), alongside a budget table. Proposals must contain the following sections:

• Project Title and Abstract: A brief, clear summary of the proposed work.
• Research Plan: A description of the research project, including its methodology and timeline.
• Broader Impact and Justification: This section is critical and must address all of the following points:
  • Importance of the Research: Make a compelling case for why this research is important and what new knowledge it will create.
  • Alignment with UMN Research Mission: Explain how the success of this project will help advance the broader research mission of the University of Minnesota, particularly by aligning with institutional strengths and priorities.
  • Advancement of Research Computing's Mission: Articulate how the successful completion of this project will contribute to the mission and sustainability of Research Computing. This could include serving as a pilot for a new technology that will more broadly benefit the UMN research community, result in external funding to build RC’s staffing, computing and data storage capacities and capabilities, and/or showcasing the value of RC's services to a new research community.
  • Path to External Funding: Describe a clear plan for how the preliminary data or findings from this seed grant will be used to leverage a larger, external sponsored funding opportunity.
  • Necessity of Seed Funding: Provide a clear justification for why this particular work cannot be supported by other existing or available external funds, and why it is a critical investment for the university to make at this time.

4. Review and Selection Process

Our ability to fund these proposals is limited, so the review process is meant to score and rank the proposals we receive with the goal of responsible stewardship of these funds.

Applications will be reviewed quarterly. Successful applicants will be notified shortly after the quarterly review meetings. 

Proposals will be evaluated based on the criteria outlined below:

• Scientific Merit
  • Is the proposal advancing scientific research in areas RC drives or supports?
  • Is the proposal's approach and timeline realistic?
• Stewardship
  • Are the resources adequate and not beyond what the objectives of the proposal are?
  • Are the resources being used appropriately: are there no alternative funding sources that can be used for this purpose?
  • Are RC resources (staff time, HPC, storage) part of the proposal, and do they meet the minimums outlined in the budgetary restrictions under Proposal Guidelines above?
• Sustainability
  • Is there an explicit plan for sustaining the work that will be done as part of this proposal beyond the end date?
• Impact
  • Are the resources produced by the grant being made available, or planned to be made available?

Additional questions that apply to specific proposal categories:

• Seed funding
  • Is there a relevant specific call mentioned in the proposal?
• Shared resource
  • Is the resource being shared with usability in mind?

Eligibility: Applicants must be a PhD candidate student at the University of Minnesota. Post-docs are not eligible for this assistantship program. The student’s preliminary oral exam must also be completed by the start of the appointment (typically no later than June 1st, 2025).

The Data Science Initiative-MnDRIVE Graduate Assistantship program supports UMN PhD candidates pursuing research at the intersection of Data Science and any of the five MnDRIVE areas: Robotics, Sensors and Advanced Manufacturing; Global Food Ventures; Advancing Industry, Conserving Our Environment; Discoveries and Treatments for Brain Conditions; and Cancer Clinical Trials. 

"Data science" in this case is defined broadly as the collection of approaches and disciplines encompassing the entire data pipeline. Proposals must align with one of the Data Science tracks:

• Foundational Data Sciences: Topics that are foundational to data science applications including data-intensive and data-informed topics and applications along with methodological research in areas such as signal processing, data mining, statistics, machine learning, and artificial intelligence (including GenAI and AI literacy)  as well as topics in ethics and privacy. Fundamental methods dealing with data storage, archiving, sharing, acquisition, compression, or transmission are included. This includes but is not limited to, disciplines that underlie data science and AI such as statistics, mathematics, computer science, philosophy, and social and behavioral sciences.
• Digital Health and Personalized Health Care Delivery: The broad scope of digital health includes disciplines such as mobile health (mHealth), real-world observational healthcare data, public health, health information technology (HIT), wearable devices or technology, virtual care, and personalized healthcare and medicine. It includes enhancements to patient and consumer health and healthcare delivery through capacity-building activities and continuous, personalized, predictive, participative, and preventive approaches.
• Agriculture and the Environment: Agriculture and the environment are closely intertwined, topics that touch either or both areas of interest. The agriculture sector faces the challenge of feeding a growing global population while minimizing environmental impact and preserving natural resources for future generations. Research challenges in this area can reduce the consequences of climate and pest risks on agricultural production, lessen the impact of pollution, soil degradation, and water contamination or trapping greenhouse gasses, and mitigating flood risks.

The funding window for FY25 has closed. Further details about this program will be provided as the next window approaches.

Eligibility:

1. Interdisciplinary Approach: Proposals should involve interdisciplinary collaboration, at least cross-departmental, however, cross-collegiate levels are preferred and demonstrate a clear integration of data science and AI methodologies.
2. Faculty and Researcher Teams: The principal investigator (PI) must be a faculty or research staff (P&A) with their primary appointment at the University of Minnesota. Collaborators from diverse backgrounds and disciplines are strongly encouraged. Adjunct or affiliated faculty are not eligible for funding under this program.
3. Two-Page Application: Applications must be concise, with no more than two pages of content (single-spaced, 12-point font), excluding references.

Funding Limit: Maximum of $15,000 available per project

The Data Science Initiative (DSI) is pleased to announce a rapid response seed grant opportunity to foster innovative research and collaboration in data science and artificial intelligence (AI). This initiative aims to catalyze projects that address emerging research needs, including projects that involve outreach and engagement, in data science and AI. Priority will be given to those proposals that align with specific external funding opportunities. Applications will be reviewed and award notifications will be made within 3 weeks of submission.

The funding window has now closed. Further details will be provided as the next window approaches.

Eligibility: Regular (Tenured and Tenure-Track) Faculty with their primary appointment at the University of Minnesota with a research area related to, encompassing, or using data science or AI throughout the UMN system are eligible to apply to be a faculty fellow. Adjunct, Affiliated, or Term Faculty are not eligible for funding under this program.

Funding Limit: DSI Faculty Fellows will receive up to $75,000 towards a course buyout, graduate research assistant support, or travel support to related conferences or professional meetings, career development, and so on. Contact [email protected] with questions concerning qualified fellowship expenses.

DSI Faculty Fellowships are intended to promote, catalyze, accelerate, and advance UMN-based collaborations in data science and AI research so that UMN faculty and staff are better prepared to compete for external funding opportunities. Faculty with research in data science and AI in all disciplines and across all campuses, including interdisciplinary collaborations intersecting with Data Science and AI are encouraged to apply for this fellowship.

For more information, or to submit an application, click the "Apply Now" button below.

The funding window has now closed. Further details will be provided as the next window approaches.

Eligibility:

• Faculty members and research teams from diverse disciplines across UMN are encouraged to apply. Faculty or research staff (P&A) with their primary appointment at the University of Minnesota throughout the UMN system are eligible to apply to be a faculty fellow. Adjunct or affiliated faculty are not eligible for funding under this program.
• Proposals must focus on the creation or expansion of data sets necessary for innovative research.
• Collaboration between multiple departments or research centers is highly encouraged.
• Plans should address how infrastructural challenges, such as data storage limits and costs, storage governance, and user access will be addressed. 

Funding Limit: Funding of up to $100,000 for projects spanning two years. Projects intending to pursue external infrastructure grants can apply for up to $200,000.

This program aims to foster research that creates or expands novel data sets, which are crucial for advancing innovative research in diverse fields. This initiative seeks to address the critical need for high-quality data by providing funding and support for projects that generate, enhance, and annotate data sets to fuel cutting-edge research at the University of Minnesota (UMN). Successful applications will demonstrate how these data sets are valuable beyond any single lab. The value may come from industry partners (particularly those based in MN or those with a strategic value in MN) or other research entities. 

For more information, or to submit an application, click the "Apply Now" button below.

The funding window has now closed. Further details will be provided as the next window approaches.

Research Computing seed grants fund informatic analysis of pilot data for future grant proposals. Seed grants fill a critical niche of funding for early-stage projects for which no other funding sources are available, and which require analysis support by Research Computing staff. Seed grants must:

• Use at least 50% of the budget for analysis by Research Computing Informatics staff
• Clearly outline how this analysis will provide a pathway to future funding, e.g. as preliminary data for specific, planned grant proposals

Funding Limit: $10,000

Proposal Components: 

Applications should include the following components:

• Title of the Project
• Principal Investigator(s) and Collaborator(s) Information
• Project Proposal (maximum 1 page, 11 point font)
  • Background and significance
  • Project objectives
  • Experimental design, data and proposed analysis
  • Expected outcomes and how these analyses will be used in planned grant proposal(s)
• A 1-page budget, with a clear justification outlining anticipated expenses.

Proposals are reviewed on a rolling basis. (Response within 3-4 weeks.) RC Informatics Seed grants are limited to Regular (Tenured and Tenure-Track) Faculty and availability of funding. 

Submissions for this program are currently closed. Further details about this program will be provided as the next window approaches.