NASA Summer 2026 Internship in Uncertainty Quantification and Machine Learning NASA Langley Research Center in United States

NASA Langley Research Center, under the leadership of Senior Computational Scientist James Warner, is recruiting motivated students for Summer 2026 internships focused on uncertainty quantification (UQ) and machine learning (ML). The team is seeking candidates for two main roles: UQ/ML Research and UQ/ML Software Engineering. Research positions involve exploring advanced methods in scientific machine learning, symbolic regression, generative modeling, and multifidelity methods, with direct application to NASA's real-world challenges. Software engineering roles focus on developing and maintaining open-source and internal NASA Python libraries, requiring strong Python skills and experience with software development best practices.

Interns will contribute to projects that address critical NASA applications such as additive manufacturing, advanced air mobility, certification by analysis, space hardware reliability, and weather-tolerant urban air mobility. The research group values skills in applied mathematics, statistics, applied physics, mechanical testing, aerospace engineering, numerical methods, and solid mechanics. High performance computing, damage science, and fatigue and fracture analysis are also part of the group's research areas.

Eligibility is limited to U.S. citizens. Graduate students and undergraduates with research experience are encouraged to apply for research roles, while those with strong Python programming and software engineering backgrounds are suited for the software engineering positions. The internship offers a unique opportunity to work on cutting-edge NASA problems in a dynamic and collaborative environment.

For more information and to apply, visit the official NASA STEM Gateway portal. The application deadline for Summer 2026 internships is October 1, 2026. No explicit funding details are provided, but NASA internships are typically paid positions. Interested applicants should prepare their materials to highlight relevant skills and experience in UQ, ML, and software development.