Type: Full-time, 12-month term
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.
Join a world-class team of scientists and engineers pushing the boundaries of materials research in a groundbreaking lab where AI and automation unlock discoveries at unprecedented speed and scale.
As a Research Scientist within the Periodic Labs experimental effort, you bring AI predictions into reality through physics and measurement science. In this role, you will both develop new material characterization approaches and be part of the team developing autonomous discovery loops.
Physical Property Measurements: Develop and perform high-fidelity thermodynamic, transport, and spectroscopic measurements of materials
High-throughput Screening: Develop rapid property measurement schemes to scale up the material characterization process
Analysis: In collaboration with our AI team, implement automated data analysis and reasoning pipelines for screened materials
Automation: In collaboration with our engineering team, develop autonomous systems for property characterization leveraging robotics
PhD in Physics, Materials Science, or related field, with 5+ years of hands-on experience in the field
Deep, demonstrated expertise in physical property characterization including electronic and magnetic measurements
Strong background in cryogenic measurements and low-noise techniques (lock-in methods, shielding/grounding, precision instrumentation).
Strong track record of performing highly impactful research, demonstrated by publications in top tier journals and/or inventions, and recognized leadership in the field
Proficiency with data analysis (e.g., Python/Jupyter, familiarity with instrument SDKs a plus) and disciplined data management.
Excellent scientific writing, collaboration across disciplines, and strong ownership of experiment quality.
Experience with automation of physical property measurements
Previous experience with computational prediction and experimental characterization design loop
Experience with lab buildout and process safety
Experience with handling data at scale
periodic-labs