Advancing Machine Learning for Scientific Discovery Fellowship

Creating Next-Generation Leaders in Machine Learning.

The Advancing Machine Learning for Scientific Discovery Fellowship is an intense 10-week program that begins around the beginning of June each summer.  AML participants build a strong foundation in modern machine learning by tackling cutting-edge research questions of importance to the Los Alamos National Lab and the world. This is a paid fellowship that includes reimbursement for travel expenses.

Applications for 2026 Fellowships can be submitted and are expected to go live on approximately November 30, 2025.

Research Fellows will learn in a hands-on fashion by engaging in scientific research using machine learning. Research will be guided by mentors with machine learning, scientific, and computational expertise. Students will work on high performance computing clusters, apply practical and state-of-the-art machine learning tools, and gain experience in communicating their work through discussions and presentations. Students will attend seminars by Los Alamos National Laboratory researchers and external visitors. We aim for high-impact summer projects that will lead to peer-reviewed, co-authored publications.

Students

This multidisciplinary program is designed for graduate and upper-level undergraduate students from all science, math, computer science, and technology fields who are seeking to incorporate machine learning into their research careers. As a general guideline, students should have a background in one of the following: probability theory, statistical methods, algorithms, or statistical learning. Experience with programming and machine learning packages is encouraged. Specific skills needed for each project are listed in the project descriptions and the application form asks which projects you are most interested in.

Eligibility Requirements

• Must be accepted to or enrolled in an undergraduate or graduate degree program
• Must have and maintain a cumulative G.P.A. of 3.2 or higher.
• Must be available to live and work in the United States.