Machine Learning - Postdoctoral Researcher

LLNL • Livermore, CA

18h • Hybrid

About The Position

Lawrence Livermore National Laboratory is seeking a Machine Learning Postdoctoral Researcher to conduct fundamental research and development in machine learning and statistical methods. The work supports interdisciplinary projects involving safety and security, foundation models for areas such as materials science or bioassurance, and uncertainty quantification for deep learning models. Projects combine state-of-the-art machine learning with scientific objectives, including multimodal sequence-to-sequence models for molecules and chemical reactions and combinations of large language models with other modalities. The researcher will also develop methods to improve model safety and trustworthiness.

The position is in the Machine Intelligence Group within the Center for Applied Scientific Computing (CASC) Division of the LLNL Computing Directorate. It is a temporary, mentored postdoctoral appointment, with possible extension up to a maximum of three years. Applicants must have received their Ph.D. or equivalent doctorate within five years of the anticipated start date. The successful candidate will work with project scientists and engineers, pursue complementary independent research, and collaborate with a broad range of scientists and external partners.

Requirements

• Must be eligible to access the Laboratory in compliance with Section 3112 of the National Defense Authorization Act.
• Recent Ph.D. in Machine Learning, Optimization, Computer Science, Mathematics, or a related field; the doctorate must have been received within five years of the anticipated start date.
• Ability and desire to develop substantial domain knowledge in application fields, communicate effectively with subject matter experts, and identify impactful applications of machine learning.
• Experience developing, implementing, and applying advanced statistical or machine learning models and algorithms using modern software libraries such as PyTorch or TensorFlow, demonstrated through medium- to large-scale deep learning models and experiments.
• Research productivity demonstrated through publications, reports, presentations, or open-source software in relevant venues.
• Experience with scientific programming in the Python ecosystem, demonstrated through software artifacts such as deep learning models, workflows, or simulations.
• Experience in one or more deep learning areas: large language models, graph neural networks, multimodal models, generative models, robustness, or explainable AI.

Nice To Haves

• Experience with high-performance computing, GPU programming, parallel programming, cloud computing, related methods, or running numerical simulations of complex workflows.
• Technical leadership in machine learning-related fields, such as mentoring or managing teams.
• Experience or interest in scientific applications such as materials science or climate science.

Responsibilities

• Research, design, implement, and apply advanced machine learning methods for multiple applications in a collaborative scientific environment.
• Participate with project scientists and engineers in defining, planning, and formulating experimental, modeling, and simulation efforts for complex national security problems.
• Propose and implement advanced analysis methodologies, collect and analyze data, and document results in technical reports and peer-reviewed publications.
• Contribute to grant proposals and collaborate with multidisciplinary teams, including academic and industrial partners, to accomplish research goals.
• Pursue independent but complementary research interests and interact with scientists inside and outside the Laboratory.
• Perform other duties as assigned.

Benefits

• Flexible benefits package
• 401(k)
• Relocation assistance
• Education reimbursement program
• Flexible schedules, depending on project needs

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree