Postdoctoral Appointee – Materials Informatics and Autonomous Synthesis

Postdoctoral Appointee to develop AI/ML methods for autonomous materials discovery and synthesis. Build machine-learning-ready data resources by integrating literature, in-house, and newly generated experimental data; design surrogate and predictive models linking composition, molecular structure, synthesis and processing conditions, morphology, and device-relevant properties; design active learning, Bayesian optimization, and other adaptive experimental design workflows to guide experiments and improve data efficiency in autonomous platforms such as the Polybot. Collaborate with experimental researchers to integrate AI/ML workflows into closed-loop autonomous synthesis, fabrication, and characterization; translate model predictions into experimental campaigns and update models with new data. Contribute to strategies for generating diverse, high-value datasets, and build reproducible computational pipelines, workflow automation, and data infrastructure supporting long-term autonomous laboratory capabilities. Share outcomes through publications, software, datasets, and internal reports.