Type: Full-time, 12-month term, possible extension
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 Associate or Postdoctoral Scientist within the Periodic Labs experimental effort, you bring AI predictions into reality through materials science, physics, and chemistry. You will work closely with AI and automation teams to accelerate and scale up materials development to enable unprecedented capabilities for scientific discovery.
Research Associates / Postdoctoral Scientists in our lab play key roles in a variety of aspects of the material discovery and development process. These include:
Material Synthesis: High-throughput synthesis and structural characterization of materials connected with AI predictions
Materials Characterization: Rapid characterization of structure and physical properties of bulk and thin film materials, feedback and interaction with AI predictions
Analysis: Partner with AI team to develop and implement automated analysis and reasoning pipelines for evaluating and predicting materials
Automation: Collaboration with engineering team to establish robotics-enabled materials development architectures
PhD in Physics, Materials Science, Solid State Chemistry, or related field.
Strong background in experimental science and data analysis
Deep expertise in one or more of synthesis, characterization and automation techniques relevant for materials R&D
Strong track record of performing highly impactful research, demonstrated by publications in top tier journals and/or inventions
Excellent scientific writing, collaboration across disciplines, and strong ownership of experiment quality.
Experience with automation in physical sciences laboratories
Experience with computational prediction and experimental verification design loop
Experience with handling data at scale
Experience with lab buildout and process safety
Previous work at National Laboratories (e.g., user facilities)
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