The Keystone Astronomy & AI Visiting Fellows Program is a new, mentored postdoctoral initiative based at the McWilliams Center for Cosmology & Astrophysics at Carnegie Mellon University. Funded by the Simons Foundation through its Targeted Grants to Institutions, the program brings together early-career researchers from around the world to advance the use of artificial intelligence in cosmological and astronomical research through month-long residencies, dual mentorship in astrophysics and AI.
About the Keystone Astronomy & AI Visiting Fellows Program
The program enables postdoctoral researchers to spend one month in residence at the McWilliams Center, where each fellow is paired with two mentors in astrophysics and AI or statistics to work on high-impact problems at the intersection of machine learning and astronomy, while also collaborating with Carnegie Mellon graduate students and co-organizing a weeklong workshop to disseminate software, datasets, and workflows to the global research community.
Funding Size
The program is funded through a targeted institutional grant from the Simons Foundation. Over the next three years, the KAAI Visiting Fellows Program will support six visiting fellows, each participating in a one-month residency. Specific stipend amounts, travel support, and benefits are expected to be part of the fellowship but will be detailed in the official application materials.
Read More: Moniepoint Women in Tech Internship Program 2026 – Apply Now
Who Can Apply
The fellowship is aimed at postdoctoral researchers and early-career scientists with a strong interest and background in both astrophysics (theoretical or computational) and artificial intelligence or statistics. Applicants should demonstrate the ability to contribute to interdisciplinary research that integrates AI methods with astrophysical data analysis, large-scale simulations, or computational modeling.
Geographic Eligibility
This is an international fellowship program. Researchers from any country are welcome to apply, provided they meet the academic and professional eligibility criteria set forth in the official application.
Read More: USDA Distance Learning and Telemedicine Grant 2026 – Apply Now
Sector or Thematic Focus
The program focuses on interdisciplinary research at the intersection of:
- Astrophysics and cosmology
- Artificial intelligence and machine learning
- Computational modeling and large-scale scientific data analysis
Projects supported by the fellowship are expected to apply trustworthy AI methods to significant problems in astrophysics, particularly those involving complex datasets, simulations, and computational challenges.
Application Process
Applications will open in spring. Prospective applicants should prepare materials demonstrating their research experience, proposed project ideas, and alignment with the program’s interdisciplinary goals. Applicants will submit these materials through the official fellowship application portal once available.
Selected fellows will be paired with two mentors from Carnegie Mellon’s astrophysics and AI/statistics communities and invited to participate in a one-month residency.
Required Materials
Typical application requirements for programs of this nature include:
- Curriculum Vitae (CV)
- Cover letter or personal statement
- Research proposal outlining the intersection of AI and astrophysics
- Letters of recommendation
- Any relevant publications or portfolio materials
Exact requirements will be specified in the official fellowship announcement.
Key Dates
- Residency periods: Scheduled throughout the next three years (six one-month residencies)
- Workshops: Scheduled at the end of each residency
Selection Notes
Fellows will be selected based on the quality and feasibility of their research proposals, demonstrated ability to work at the intersection of AI and astrophysics, and potential for interdisciplinary collaboration. Successful applicants will also show promise in contributing to and benefiting from the collaborative research environment at Carnegie Mellon and the broader scientific community.