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About Periodic Labs

We’re an AI and physical sciences company building state-of-the-art models to accelerate breakthroughs across materials, energy, and beyond. Backed by world-class investors and growing rapidly, we operate at the pace the frontier requires. Our team brings deep expertise, genuine ownership, and an insatiable drive to push the boundaries of what’s scientifically possible.

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 use theoretical modeling to connect first-principles calculations and experiments. You will collaborate closely with computational and experimental scientists and ML researchers to develop physical understanding that guides and accelerates the discovery of novel quantum materials.

What You’ll Do

• Develop and apply theoretical models to interpret experimental observations and guide materials discovery efforts
• Bridge first-principles calculations (e.g., DFT) and experimental results to build predictive physical understanding
• Collaborate with ML researchers to incorporate theoretical insights into machine learning models and inform training data strategies
• Work with computational and experimental scientists to design experiments and validate theoretical predictions
• Communicate theoretical findings clearly across disciplines and contribute to a shared scientific roadmap

You Will Thrive in This Role If You Have

• PhD in condensed matter theory, with a focus on quantum materials
• Deep expertise in relating theoretical models to real materials and experimental observables
• Strong publication record demonstrating impactful, independent research
• Ability to collaborate effectively across theory, computation, and experiment

Especially Strong Candidates May Also Have

• Experience running first-principles calculations such as density functional theory (DFT) on realistic systems
• Experience with deep learning methods, including graph neural networks, applied to materials or physics problems
• Experience modeling superconductivity and/or magnetism in quantum materials
• Familiarity with high-throughput computational workflows or materials databases

Mechanics