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Periodic Labs
Verified live 2d ago

Research Scientist, Condensed Matter Theory

Brief overview

United StatesIn-person
PhDOr in progress
2 H-1B approvalsDept. of Labor
theoretical modelingfirst-principles calculationsdensity functional theorydeep learninggraph neural networksmodeling superconductivitymodeling magnetism

About the company

Periodic Labs logo
Periodic Labsperiodic.com

Periodic Labs develops artificial intelligence systems that simulate and predict the properties of materials using machine learning.

Visa sponsorship history

2 years sponsoring, last filed FY2026

Data powered by U.S. Department of Labor. This does not guarantee sponsorship for this specific role.
2H-1B approved
100%approval rate
$250,000median wage / yr
H-1B Petition ApprovalsVisas USCIS actually granted: the strongest sign the company sponsors.
20252
LCA Certified ApplicationsAn early filing step, not a visa approval: it signals intent, not confirmed sponsorship.
20261
Top sponsored roles
Research Engineer, Lab Automation

Job description

About Periodic Labs We are an AI + physical sciences lab building state of the art models to make novel scientific discoveries. We are well funded and growing rapidly. Team members are owners who identify and solve problems without boundaries or bureaucracy. We eagerly learn new tools and new science to push forward our mission.About the Role

Join a world-class team of scientists and engineers pushing the boundaries of physics research in a groundbreaking lab where AI, theory, and automation unlock discoveries at unprecedented speed and scale.

As a Research Scientist in Condensed Matter Theory, you will work on using theoretical modeling to connect first-principles calculations and experiments. You will collaborate with computational and experimental scientists and ML researchers.

Qualifications

  • PhD in formal condensed matter theory for quantum materials.

  • Deep expertise in relating theoretical models to real materials and experiments.

Bonus Qualifications

  • Experience with running first principles calculations like density functional theory on realistic systems.

  • Experience with deep learning, including but not limited to graph neural networks.

  • Experience in modeling superconductivity and/or magnetism.

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