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Machine Learning - Postdoctoral Researcher

Lawrence Livermore National Laboratory - Livermore, CA, United States

Posted Jun 10, 2026

Benefits

Parental leave
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Salary
$138K-$138K not verified - source not recorded; timestamp not recorded
401(k) match
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About this role

Machine Learning - Postdoctoral Researcher 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're looking for a Machine Learning Postdoctoral Researcher to contribute to fundamental R&D in machine learning and statistical methods in support of different projects related to AI Safety & Security, Foundation Models in areas such as material science or bio assurance, and uncertainty quantification for deep learning models. These will be interdisciplinary projects that aim to combine state-of-the-art machine learning models with various science objectives. Examples are multi-modal sequence-to-sequence models for molecules and chemical reactions or combine large language models with other modalities. Furthermore, you will develop methods to improve safety and trustworthiness of these models. This position will be in the Machine Intelligence Group in the Center for Applied Scientific Computing (CASC) Division within the LLNL Computing Directorate. You will Research, design, implement, and apply advanced machine learning methods for multiple applications in a collaborative scientific environment. Actively participate with project scientists and engineers in defining, planning, and formulating experimental, modeling, and simulation

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