Training future addiction researchers in AI and machine learning techniques
Official title: Preparing the Next Generation of Addiction Researchers in Computational AI/ML Techniques
Funded at University of North Texas Hlth Sci Ctr · NIH-11015369 · Data updated 2026-11-12
This study is all about helping future addiction researchers learn how to use smart computer tools to better understand and tackle addiction challenges, so they can make a real difference in people's lives.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of North Texas Hlth Sci Ctr NIH-funded |
| Lab location | 1 site (Fort Worth, United States) |
| Project start | September 30, 2024 |
| Project end | July 31, 2029 |
| Funding year | 2024 |
| Award (this year) | $134,311 |
| NIH project number | 1R25DA061742-01 |
| Project ID | NIH-11015369 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals pursuing careers in addiction research, particularly those interested in data science and computational methods.
✕Less likely to help
Patients who are not involved in addiction research or do not have an interest in data science may not benefit from this research.
What this research studies
This research focuses on developing educational programs to equip the next generation of addiction researchers with skills in artificial intelligence (AI) and machine learning (ML). It aims to address challenges in addiction research, such as small data sets and complex data types, by teaching researchers how to effectively use advanced computing algorithms. The program will promote Open Science practices and enhance the ability of researchers to tackle pressing questions in the field of addiction. Participants will engage in hands-on learning and collaborative projects to apply these techniques in real-world scenarios.
Why it matters
Potential benefit: If successful, this research could lead to more effective addiction treatments and interventions through improved data analysis and understanding.
How similar studies have gone: Other educational initiatives in data science and AI have shown success in enhancing research capabilities, indicating a promising outlook for this approach.
Where this research is happening
-
University of North Texas Hlth Sci Ctr
Fort Worth, United States Active
Researchers
- Principal investigator: Mun, Eun-Young — University of North Texas Hlth Sci Ctr
- Study coordinator: Mun, Eun-Young
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About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.