Postdoc: Adaptive Generative Modeling for Science

Los Alamos National Laboratory; Los Alamos (NM); On-site; USD 87,000 - 107,000; Full time

Benefits offered by this job

Medical insurance

Dental and vision

Paid time off

Job summary

Los Alamos National Laboratory's Information Sciences Group (CAI-3) in the Computing and Artificial Intelligence Division seeks a postdoctoral researcher to advance modern machine learning methods for complex spatiotemporal physical-science problems, including diffusion and related generative models.

The role involves collaboration across mathematics, ML, statistics, computational physics, materials science, and accelerator physics.

Qualifications

• PhD in a theoretical or computational physics, applied mathematics, materials science, engineering, or related quantitative field.
• Strong track record in ML research and publications or substantial projects.
• Programming experience in Python or Julia and modern ML libraries.

Responsibilities

• Develop, implement, and evaluate ML and generative models for scientific data.
• Work with large experimental and simulation datasets from multiple physical-science domains.
• Develop high-quality research software for large-scale computing platforms.
• Publish original research in leading journals and present results at conferences.
• Collaborate with ML researchers and domain scientists on multidisciplinary projects.

Skills

PhD in theoretical/computational/phys

Machine learning methods

Python (PyTorch)

Collaboration skills

Education

PhD in a quantitative physical-science field

Tools

PyTorch

TensorFlow

Keras

JAX

Job description

Los Alamos National Laboratory's Information Sciences Group (CAI-3) in the Computing and Artificial Intelligence Division seeks a postdoctoral researcher to advance modern machine learning methods for complex spatiotemporal physical-science problems, including diffusion and related generative models.

The role involves collaboration across mathematics, ML, statistics, computational physics, materials science, and accelerator physics.