Location: Lemont, IL
Cap-Exempt H-1B Position — No lottery required
The Center for Nanoscale Materials (CNM) at Argonne National Laboratory invites applications for a postdoctoral research position focused on developing AI/ML methods for autonomous materials discovery and synthesis. We are seeking a creative and collaborative researcher who is excited by the opportunity to help shape the future of autonomous synthesis and self-driving laboratories. This role is ideal for someone who enjoys working at the intersection of data science, machine learning, materials research, and experiment, and who is motivated to translate computational advances into real laboratory workflows. The position will focus on building the data resources, predictive models, and closed-loop decision frameworks needed to accelerate experimentation and advance next-generation autonomous laboratories. The broader goal is to enable AI-driven materials discovery, autonomous synthesis, and the development of high-quality, reusable datasets that support adaptive experimentation and long-term scientific impact. This research may include applications in areas such as organic electrochemical and neuromorphic devices, but the central emphasis is on creating data-driven methods and infrastructure that can guide experiments, improve efficiency, and strengthen collaboration between computation and experiment. **Key Responsibilities** - Develop machine learning-ready data resources for materials by integrating literature, in-house, and newly generated experimental data - Build surrogate and predictive models that connect composition, molecular structure, synthesis and processing conditions, morphology, and device-relevant properties - Design active learning, Bayesian optimization, uncertainty-aware modeling, and other adaptive experimental design workflows to guide experiments and improve data efficiency in autonomous platforms such as the Polybot - Work closely with experimental researchers to integrate AI/ML workflows into closed-loop autonomous synthesis, fabrication, and