For Students: Apply to Join a Faculty-Led Data Intensive Research Project
Are you interested in gaining hands-on experience in data science, AI, or machine learning applications? This program offers an exciting opportunity to work directly with a faculty mentor on an interdisciplinary research project throughout the spring semester.
What to expect:
- Collaborate with a faculty research team on a real-world project.
- Apply and expand your data science and AI/ML skills in a research context.
- Receive a $3,000 stipend for your participation. The stipend will be paid when the period reports and research project are completed.
- Contribute to innovative research with the potential for publication, presentations, or continued involvement.
Who should apply:

Undergraduate and graduate students from all disciplines who have experience or strong interest in data science, machine learning, or computational methods.
When completing the application, be sure to include how your skills and interests align with working on data and interdisciplinary research questions!
For Faculty: Submit a Project Proposal and Mentor a Student Researcher
Faculty are invited to propose research projects that would benefit from student collaboration in data science or AI/ML which leverages the PA Science DMZ networking infrastructure. Potential faculty PIs will submit short project descriptions outlining their research goals, data needs, and the role a student researcher could play. The goal of the PA Science DMZ network effort is to enhance cyberinfrastructure connectivity and support research fields such as data analytics, robotics, cybersecurity, molecular dynamics, and linguistics. By addressing infrastructure and connectivity gaps in participating institutions, the project aims to foster collaboration and innovation, enabling smaller institutions to access critical scientific tools.
What to expect:
- Faculty projects and student applications will be reviewed and matched based on research interests and skill sets.
- Mentors will guide students through data analysis, modeling, or computational exploration relevant to the project.
- Faculty participation strengthens interdisciplinary connections across our science network and provides valuable mentorship experiences.
Next steps:

Please identify potential projects and encourage students to apply! Application and matching timelines will be shared soon.