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.

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

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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.

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

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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. Please be aware that the same process will take place again in 2025. 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. Please be aware that the same process will take place again in 2025. Further details will be provided as the next window approaches.