Graduate Assistantships

The DSI offers Graduate Assistantships aimed at harnessing the expertise of UMN graduate students to advance data science and AI research.

These programs not only support individual academic and professional growth but also contribute significantly to the broader research community by fostering innovation, collaboration, and practical application of data science and AI across various disciplines. Graduate assistants play a crucial role in advancing the DSI's mission by engaging in cutting-edge research, supporting key initiatives like the AI Office Hours, and promoting data literacy and science outreach. We seek dedicated individuals passionate about driving forward the field of data science through these impactful assistantship opportunities.

DSI-MnDRIVE PhD Graduate Assistantship Program 2025

NOTE: It is recommended to log in with your UMN Credentials using the "UMN Login" in the top right before clicking "Apply".

Eligibility Requirements

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).

Program Description

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 program aims to support approximately ten 12-month research assistantships for a 12-month (2 semesters and 1 summer), 50% graduate research assistantship, including tuition for up to 14 credits each semester (Fall & Spring) and subsidized health insurance through the Graduate Assistant Health Plan. Depending on the number of applicants and the availability of funding, smaller stipends may also be offered. Applications for assistantships may begin as early as the following Spring term and must be started by June 1 of the year following the submission of your application. Graduate students are expected to have successfully completed their preliminary oral exam by the start of the appointment. Depending on the availability of funding, awards may include up to $1,500 for travel to a conference or to defray costs for specialized training support if they are directly related to the proposed research. A final report of accomplishments made possible by this support is expected after the completion of this award.

Selection Criteria:

Applicants will be selected based on academic record, potential for future research productivity, and impact on the areas of the MnDRIVE initiative (see mndrive.umn.edu for the goals of each area).

Application Checklist:

The application process will require the submission of the following materials.

Public-facing project abstract (200 word limit) (If funded, this abstract will be used to create a Research Computing Project Highlight and DSI News story.)
1-page research plan (single-spaced, 11-point font), describing alignment with MnDRIVE goals and intersection with data science
1-page personal statement (single-spaced, 11-point font), describing how this award will advance your career and meet the goals of MnDRIVE
Curriculum Vitae, which includes previous education, relevant work experience, honors/awards, research experiences, publication, and conference abstract titles
Unofficial transcript
Two letters of recommendation (each letter should be no more than 2 pages): Applicants should arrange for two letters recommendation, one of which must be from the applicant's faculty advisor. The letters should comment on the applicant’s potential for future research productivity. The faculty advisor should (i) include confirmation that they will continue to be the advisor of the applicant during the period of funding, (ii) confirm that the applicant will have reached doctoral candidacy by the start of the award, (iii) confirm the research plan, and (iv) explain the impact or implications of the research for at least one MnDRIVE area.

Completed applications and recommendation letters must be submitted through this application portal by no later than October 4 at 5:00pm CDT. Late applications will not accepted. Fellowship awards will be announced by early December.

For questions, please contact [email protected]

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DSI-AI Ambassador Graduate Assistantship Program

2024-5 Program Application Deadline

The Data Science Initiative-AI Graduate Assistantship program supports UMN graduate students with expertise in Artificial Intelligence and Data Science, from any discipline.

Eligibility Requirements:

Applicant must be a current graduate student in good standing at the University of Minnesota Twin Cities Campus. Post-docs are not eligible for these awards.

Program Description:

DSI-AI Ambassador Graduate Assistantship Program

This GA program will support the DSI AI Office Hours program. Graduate Assistants will be required to assist with the DSI and Minnesota Supercomputing Institute’s jointly organized bi-weekly AI High Performance Computing (HPC) office hours by helping attendees understand general AI concepts, how to pull down and use a model, how to train a Generative AI model, and how to use Python to manipulate data (i.e., use tools such as pandas).

The graduate assistant will also serve as a representative for the DSI and champion for Data Science and AI literacy in their field of study. During the summer term, ambassadors will be expected to plan, develop, and deliver engagement activities to improve Data Science and AI literacy under the mentorship of the DSI team. These activities can include office hours, tutorials, workshops, poster sessions, and more. The DSI will work with Ambassadors to find the right type of engagement that fits with their goals and the needs of their department or community. Other opportunities could include outreach to K-12 students and educators, and developing and sharing educational materials that improve interest, understanding, utilization, or implementation of data science techniques, methods, and tools. Ambassadors often contribute to social media and marketing efforts aimed at prospective and current students. They may share their experiences through blog posts, vlogs, or social media posts to give others a glimpse into how they utilize data science in their academic career. Materials and programs created by ambassadors form resources that future students and ambassadors may use or extend in future years.

Application Process:

Apply for this position under Job Opening 363751 on the University of Minnesota Jobs Website.

External Applicants: Apply Here
Current Employees: Apply Here

Application Materials:

Cover Letter:
- One page detailing your experience with AI, LLMs, Python (or other programming languages), and any other relevant qualifications.
- At least one paragraph describing your ideas for engagement activities to improve Data Science and AI literacy at UMN. (Upload as your 'Cover Letter' in the job link.)
Resume/CV
Unofficial Transcript
Letter of Recommendation:
- A letter from a UMN faculty member.
- The faculty member should send this as a separate email to [email protected] with the subject line: “DSI-AI Ambassador GA Application: YOUR_NAME” (replace "YOUR_NAME" with the student's name).

Requirements:

During the fall and spring semesters:

Assist with bi-weekly AI HPC office hours (4 hrs)
Regular sync-up meeting (monthly, 1 hr)
During the summer:
Plan and help execute a set of AI ambassador materials and activities with the DSI team

Job Duties:

75% Assistant for AI Makerspace Hours

Attend and assist in-person AI makerspace hours approximately every other Friday during the Fall and Spring Semester
Guide students, faculty, and staff who attend through AI-provided tutorials
Answer questions on programming and AI in Python, R, and other languages
Show attendees how to use LLMs and guide them through a set of learning exercises
Work with DSI and MSI staff to identify issues in the program and events and create solutions

25% Data Science Ambassador

Plan, develop, and deliver engagement activities to improve Data Science and AI literacy with the assistance of the DSI team

Funding Limit:

The maximum award value will be a 12-month (2 semesters and 1 summer), 25% graduate research assistantship, including tuition for up to 14 credits each semester (fall & spring), and subsidized health insurance through the Graduate Assistant Health Plan.

Qualifications:

Knowledge of Python programming
Expertise in Artificial Intelligence
Interest and ability to assist in teaching learners from all domains (i.e., not necessarily CSE students) with patience and grace
Strong communication skills
Knowledge of R programming language is a plus
Understanding how to use the MSI HPC (e.g., submit jobs) is a plus

The DSI offers Graduate Assistantships aimed at harnessing the expertise of UMN graduate students to advance data science and AI research.

DSI-MnDRIVE PhD Graduate Assistantship Program 2025

DSI-AI Ambassador Graduate Assistantship Program

2024-5 Program Application Deadline

Eligibility Requirements:

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