Non-Student Research Assistant

Title

Non-Student Research Assistant

Non-Pos Hourly Posting Code

Denton

UNT-Provost

UNT-College of Information

UNT-Data Science-133330

Denton

$18.00 an hour

0.48

Not Retirement Eligible

Welcome to the University of North Texas System. The UNT System includes the University of North Texas in Denton and Frisco, the University of North Texas at Dallas and UNT Dallas College of Law, and University of North Texas Health at Fort Worth. We are the only university system based exclusively in the robust Dallas-Fort Worth region. We are growing with the North Texas region, employing more than 14,000 employees, educating a record 49,000+ students across our system, and awarding nearly 12,000 degrees each year.

We are one team comprised of individuals who are committed to excellence, curiosity and innovation. We are transforming lives and creating economic opportunity through education. We champion a people-first values-based culture where We Care about each other and those we serve. We believe that we are Better Together because we foster an environment of respect, belonging, and access for all. We demonstrate Courageous Integrity through setting exceptional standards and acting in the best interest of our communities.

We are encouraged to Be Curious about opportunities for learning, creating, discovering, and innovating, and are encouraged to learn from failure. Show Your Fire by joining our team and exhibiting your passion and pride in your work as part of our UNT System team.

Learn more about the UNT System and how we live our values System.edu.

The Department of Information Science at the University of North Texas (UNT), part of the College of Information, offers nationally ranked, iSchool-accredited programs focusing on the intersection of people, technology, and information. It provides diverse, flexible degree paths—including BS, MS, and the largest interdisciplinary Ph.D. in the US—in Library Science, Information Science, Data Science, and Health Informatics.

The Research Assistant will contribute to advanced data science and AI-focused research projects within the UNT NSF IUCRC Electric, Connected and Autonomous Technologies for Mobility (eCAT Center). The RA will work under faculty supervision to conduct research on multimodal data processing and learning and other data science research assigned by the faculty advisor.

• Master’s in Data Science, Computer Science, or a closely related field
• Prior coursework and working experience in data science, machine learning, statistics, or related areas
• Proficiency in Python for data analysis and modeling, machine learning, and data visualization.

• Strong proficiency in Python and data science libraries (e.g., pandas, Num Py, scikit-learn, matplotlib/seaborn) and data visualization tools such as Microsoft Fabric.
• Experience with exploratory data analysis, feature engineering, and model evaluation
• Understanding of machine learning or statistical modeling techniques
• Familiarity with research workflows, including experiment design and reproducibility
• Experience using Jupyter Notebooks, Git/Git Hub, or similar tools
• Ability to read, analyze, and synthesize academic research literature
• Strong written communication skills for research documentation
• Ability to manage multiple research tasks and meet deadlines
• Self-motivated with the ability to work independently under faculty guidance

None specified.

• Support faculty-led data science and AI research projects
• Design and implement data preprocessing, fusion, and analysis pipelines
• Develop, train, and evaluate machine learning and statistical models
• Assist with experiment tracking, result analysis, and reproducibility
• Use data visualization tools to visualize data analysis and learning results
• Contribute to technical reports
• Maintain well-documented codebases and organized research datasets
• Participate in regular research meetings and present progress updates
• Perform other research-related responsibilities as assigned

• Communicating with others to exchange information.
• Lifting and Moving objects up to 20 pounds.
• Sedentary work that primarily involves sitting/standing.

• No adverse environmental conditions expected.

Up to 20 hours per week. Flexible schedule coordinated with the supervising faculty member. Hybrid possible depending on research needs.

No

This is a Security Sensitive Position.

Applicants must submit a minimum of two professional references as part of their application. If needed, additional references can be added after the application has been submitted.

For information regarding our Benefits, .

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