Information Assurance Representative

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., * 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 characterization; translate model predictions into experimental campaigns; and update models using newly acquired data
• Contribute to strategies for generating diverse, high-value datasets, identifying meaningful descriptors and representations, and building reproducible computational pipelines, workflow automation, and data infrastructure that support long-term autonomous laboratory capabilities
• Share research outcomes through publications, presentations, software, datasets, and internal reports, Argonne employees, and certain guest researchers and contractors, are subject to particular restrictions related to participation in Foreign Government Sponsored or Affiliated Activities, as defined and detailed in United States Department of Energy Order 486.1A. You will be asked to disclose any such participation in the application phase for review by Argonne’s Legal Department.

All Argonne offers of employment are contingent upon a background check that includes an assessment of criminal conviction history conducted on an individualized and case-by-case basis. Please be advised that Argonne positions require upon hire (or may require in the future) for the individual be to obtain a government access authorization that involves additional background check requirements. Failure to obtain or maintain such government access authorization could result in the withdrawal of a job offer or future termination of employment.

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• Demonstrated accomplishments in materials informatics, scientific machine learning, or AI-guided experimental design
• Strong Python and scientific computing skills, including experience with tools such as NumPy, pandas, scikit-learn, and machine learning frameworks such as PyTorch, TensorFlow, or similar
• Experience developing surrogate models, predictive models, or adaptive learning workflows for scientific or engineering applications
• Strong interest in working closely with experimental researchers in a laboratory-centered environment
• Evidence of independent research productivity through publications, software, datasets, or similar outputs
• Excellent communication skills, the ability to work effectively in interdisciplinary teams
• Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork, * Experience with active learning, Bayesian optimization, adaptive experimental design, reinforcement learning for experiments, or uncertainty quantification
• Experience with autonomous, self-driving, or robotic laboratory platforms
• Background in electronic polymers, conjugated polymers, organic semiconductors, soft materials, electrochemical materials, or related functional materials
• Experience integrating literature, experimental, and simulation datasets into unified, machine learning-ready workflows
• Familiarity with cheminformatics or polymer informatics, molecular representations, descriptor engineering, RDKit, characterization-informed modeling, multimodal data fusion, interpretable machine learning, NLP, text mining, or automated extraction of materials data from the literature
• Experience with workflow automation, data infrastructure, database development, reproducible research pipelines, and collaborative environments that span computation, data science, and experiment

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