Co-Op, Agentic Simulations for Materials Science
Core
Develop ML- and agent-driven infrastructure for autonomous physics simulations in materials science and chemistry.
Role type
Co-op, agentic simulations for materials science
Builds
Intelligent pipelines for in silico materials discovery connecting physics-based simulations, generative models, and agentic AI systems.
Domain
Materials science and chemistry
Deliverable
production ML models
Required skills
Python, deep learning frameworks, end-to-end workflow deployment, applied ML research in physical sciences, code practices
Preferred skills
ML interatomic potential development, LLMs for materials science, generative models for materials, high-throughput computational materials discovery, open-source simulation infrastructure contributions, materials science simulation codes, workflow orchestration frameworks
Responsibilities
Develop methods and workflows for in silico materials discovery; Build intelligent pipelines where AI agents can design, launch, interpret, and refine simulations autonomously; Design new methods to accelerate simulations with ML-based surrogate models; Collaborate with computational scientists, machine learning experts, and platform engineers to integrate discovery pipelines.
Seniority
Co-op (undergraduate or graduate student)
