Machine Learning Physics Graduate Student

Job Information

Internship | Intern

Students | Livermore, CA | 06/02/2026

Reference #: REF
8405T

Job Code: 705.2 Grad Student R&D/Technical / 712.0 Intern Exempt

Organization: Physical and Life Sciences

Position Type: Student Intern

Security Clearance: None/Position does not require US citizenship (assignments longer than 179 days require a federal background investigation)

Drug Test: Required for external applicant(s) selected for this position (includes testing for use of marijuana)

Medical Exam: Not applicable Apply Now

Join us and make YOUR mark on the World!

Lawrence Livermore National Laboratory (LLNL) has turned bold ideas into world‑changing impact advancing science and technology to strengthen U.S. security and promote global stability.

Our mission spans four critical national security areas nuclear deterrence, threat preparedness, energy security, and multi‑domain defense empowering teams to take on the toughest challenges of today and tomorrow. With a culture built on innovation and operational excellence, LLNL is a place where your expertise can make a real impact.

We have multiple openings for Machine Learning
Graduate Student Interns to engage in practical research experience to further their educational goals. You will work on multidisciplinary projects, such as development of classical empirical and machine learning interatomic potentials, discovery of partial differential equations (PDEs), numerical solutions of partial differential equations to model material behavior at continuum scale and analysis of large atomic datasets. These positions are in the Equation of State Materials Theory Group of the Physics Division of the Physical & Life Sciences Directorate.

Full‑time on‑site presence is required due to the nature of the work.

• Develop parallel C/C++/Python codes to train, test and evolve (a) PDEs (for phase field and phase field crystal models) discovered from data, and (b) interatomic potentials developed from quantum simulations.
• Explore the use of machine learning methods to discover and evolve PDEs for phase field and phase field crystal models.
• Analyze results, provide weekly updates and present work at poster sessions.
• Review literature in the field of study, document results and write papers.
• Perform other duties as assigned.

• Must be eligible to access the Laboratory in compliance with Section 3112 of the National Defense Authorization Act (NDAA).
• Continuing student in good standing at an accredited institution of higher education pursuing a graduate degree in Physics or related field.
• Research background with a record of publication.
• Experience in writing codes (in C/C++ and Python) and a background in Materials Science/Engineering/Physics/Applied Mathematics.
• Excellent skills in written and verbal communication, as well as teamwork.

• Experience in parallel computing, porting codes to GPUs, experience in numerical solutions of partial differential equations.

$6,752 - $8,201 Monthly

This position is under a step structure. Step placement is determined by your most recent completed academic year.

#LI-Onsite

To be qualified for this position, candidates must be eligible to access the Laboratory in compliance with Section 3112 of the National Defense Authorization Act. Restrictions prohibit citizens of China, Russia, Iran and North Korea without dual U.S. citizenship or legal permanent residence from accessing specific non‑public areas of national security or nuclear weapons facilities.

External applicant(s) selected for this position must pass a post‑offer, pre‑employment drug test, including testing for use of marijuana.

Depending on job duties, you may be required to work in a Limited Area where personal mobile devices are prohibited. Allowed devices include hearing aids without wireless capabilities or with disabled wireless functions. For medical devices paired with a mobile device, separate approval may be required.

We are an equal opportunity employer that is committed to providing…