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Lawrence Livermore National Laboratory · 3 months ago

Machine Learning - Postdoctoral Researcher

Livermore, CA

Full-time

Hybrid

Mid, Senior Level

$138K/yr - $138K/yr

Lawrence Livermore National Laboratory (LLNL) is dedicated to strengthening the United States' security through innovative solutions. They are seeking a Machine Learning Postdoctoral Researcher to contribute to R&D in machine learning and statistical methods, focusing on AI Safety & Security and interdisciplinary projects. The role involves research, collaboration with scientists, and developing advanced methodologies for national security applications.

Information TechnologyMarket ResearchSecurity

Growth Opportunities

No H1B

Security Clearance Required

Responsibilities

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 efforts for complex problems stemming from national security applications

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 others in a multidisciplinary team environment, including academic and industrial partners, to accomplish research goals

Pursue independent (but complementary) research interests and interact with a broad spectrum of scientists internal and external to the Laboratory

Perform other duties as assigned

Qualification

Machine LearningStatistical MethodsDeep LearningPython ProgrammingPyTorchTensorFlowHigh-Performance ComputingScientific ApplicationsTechnical LeadershipCollaboration

Required

Must be eligible to access the Laboratory in compliance with Section 3112 of the National Defense Authorization Act (NDAA). See Additional Information section below for details

Recent Ph.D. in Machine Learning, Optimization, Computer Science, Mathematics or a related field

Demonstrated ability and desire to obtain substantial domain knowledge in fields of application to enable effective communication with subject matter experts, and to identify novel, 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, TensorFlow, or similar as evidence through medium to large scale deep learning models and experiments

Demonstrated research productivity, as documented by publications, reports, presentations, and/or open-source software in relevant venues (NeurIPS, ICML, ICLR, CVPR, AAAI, AISTATS, UAI, KDD, JMLR, Nature etc.)

Experience with scientific programming in the Python ecosystem as evidence through software artifacts, such as deep learning models, workflows, simulations, or similar

Experience with one or more of the following areas of deep learning: large language models, graph neural networks, multimodal models, generative models, robustness, explainable AI

Preferred

Experience with high-performance computing, GPU programming, parallel programming, cloud computing, and/or related methods including running numerical simulations of complex workflows

Demonstrated technical leadership in fields related to machine learning, such as mentorship or managing teams

Experience or interest in scientific applications, such as, material science, climate science, etc