Lawrence Livermore National Laboratory (LLNL) is seeking a Machine Learning Postdoctoral Researcher to advance fundamental R&D in machine learning and statistical methods for AI Safety & Security, Foundation Models in areas like material science or bio assurance, and uncertainty quantification for deep learning models. These projects aim to integrate state-of-the-art machine learning with scientific objectives and improve the safety and trustworthiness of AI models.
Requirements
- 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.
- 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
- Experience with high-performance computing, GPU programming, parallel programming, cloud computing, and/or related methods including running numerical simulations of complex workflows
- Experience or interest in scientific applications, such as, material science, climate science, etc.
- 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.
Responsibilities
- Research, design, implement, and apply advanced machine learning methods for multiple applications in a collaborative scientific environment.
- 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.
- Develop methods to improve safety and trustworthiness of these models.
- 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.
Other
- Must be eligible to access the Laboratory in compliance with Section 3112 of the National Defense Authorization Act (NDAA).
- 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.
- 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.
