Postdoc: AI-Driven Materials Scientist on HPC

Los Alamos National Security LLC; Los Alamos (NM); On-site; USD 85,000 - 100,000; Full time

Benefits offered by this job

Medical insurance

Dental and vision insurance

Life and disability insurance

Parental leave

401(k) matching

Tuition assistance

Flexible schedules and PTO

Relocation packages

Job summary

Los Alamos National Laboratory seeks postdoctoral researchers to advance machine learning interatomic potentials for catalysis, electrochemistry, polymers, and chemical reactivity. You will train and validate models using LANL data and public datasets, then deploy them on HPC clusters like El-Capitan to support mission-critical predictions.

The role includes collaboration with theorists and experimentalists, proposal writing, and presenting modeling results at conferences.

Qualifications

• A PhD in Computer Science, Applied Mathematics, Physics, Chemistry, Materials Science, or related fields, completed within the last five years or soon to be completed.
• Strong programming skills, notably in Python and deep learning libraries (JAX/PyTorch/TensorFlow).
• Experience with machine learning models and conducting original scientific research with publications.

Responsibilities

• Develop and apply ML interatomic potentials (MLIPs) for predicting chemical and material properties.
• Deploy models on HPC clusters (e.g., El-Capitan) to impact LANL mission needs.
• Contribute to proposal writing, program development, manuscript preparation, and conference presentations.

Education

PhD in Computer Science, Applied Mathematics, Physics, Chemistry, Materials Science, or related fields

Tools

Python

JAX

PyTorch

TensorFlow

CUDA

Job description

Los Alamos National Laboratory seeks postdoctoral researchers to advance machine learning interatomic potentials for catalysis, electrochemistry, polymers, and chemical reactivity. You will train and validate models using LANL data and public datasets, then deploy them on HPC clusters like El-Capitan to support mission-critical predictions.

The role includes collaboration with theorists and experimentalists, proposal writing, and presenting modeling results at conferences.