Co-Op, Agentic Simulations for Materials Science

What you'll do

• Develop workflows connecting physics-based simulations, generative models, and agentic AI for in silico materials discovery.
• Build AI pipelines that design, launch, interpret, and refine simulations autonomously.
• Develop ML surrogate methods to accelerate simulations and integrate discovery pipelines with the broader scientific platform.

What they ask for

• Currently enrolled in an MS or PhD program in a listed or related field; exceptional BS candidates with research experience may be considered.
• Proficiency in Python, deep learning frameworks, and end-to-end workflow deployment.
• Applied ML research in physical sciences, including method development, model training and evaluation, and sound coding practices.
• Strong communication and presentation skills.

Nice to have

• Prior experience or publications in ML interatomic potentials, LLMs for materials science and chemistry, generative models for materials, or high-throughput computational materials discovery.
• Open-source simulation or ML infrastructure contributions, or familiarity with materials simulation codes and workflow orchestration frameworks.

Tools

Python

AI focus

Develop AI agents that orchestrate simulations and ML methods, including surrogate models, for autonomous in silico materials discovery.