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Machine Learning Physics Graduate Student

Lawrence Livermore National Laboratory - Livermore, CA, United States

Posted Jun 10, 2026

Benefits

Parental leave
Not verified
Non-birth-parent leave
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Family-building benefits
  • Fertility benefits: Not verified
  • Adoption assistance: Not verified
  • Surrogacy assistance: Not verified
Mental health support
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Relocation assistance
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Childcare support
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Learning budget
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Verification
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Salary
$7K-$8K/mo From the posting source checked Jun 20, 2026
401(k) match
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Market context

U.S. role benchmark (BLS OEWS)
$55,390 U.S. median for this role
Projected growth (BLS Employment Projections)
-0.1% - Decline

62% above the BLS role benchmark for teaching and education aggregate.

Matched to SOC 25-2021 - Teaching and Education aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function
Teaching Education From the posting source checked Jun 20, 2026
Seniority
Mid From the posting source checked Jun 20, 2026

Schedule

Shift type
Not verified
Weekend work
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Company

Company stage
Public-company From the posting source checked Jun 20, 2026

Application

Cover letter
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Assessment
Not verified
Deadline
Not stated

Where they hire

State eligibility is not yet verified.

About this role

Machine Learning Physics Graduate Student Livermore, CA, United States Company Description: 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. Job Description: 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 in the Equation of State Materials Theory Group of the Physics Division of the Physical & Life Sciences Directorate. This position requires full-time on-site presence due to the nature of the work. You will 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

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