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 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

Position Requirements

Recent or soon-to-be-completed PhD (within the last 0-5 years) in chemistry, chemical engineering, materials science, polymer science, physics, computer science, and/or data science
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

Preferred Qualifications

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

Application Materials

Updated CV/Resume
Unofficial Ph.D. transcripts
If already awarded, a copy of the Ph.D. diploma

Job Family

Postdoctoral

Job Profile

Postdoctoral Appointee

Worker Type

Long-Term (Fixed Term)

Time Type

Full timeThe expected hiring range for this position is $72,879.00-$121,465.00.

Please note that the pay range information is a general guideline only. The pay offered to a selected candidate will be determined based on factors such as, but not limited to, the scope and responsibilities of the position, the qualifications of the selected candidate, business considerations, internal equity, and external market pay for comparable jobs. Additionally, comprehensive benefits are part of the total rewards package.

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As an equal employment opportunity employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a safe and welcoming workplace that fosters collaborative scientific discovery and innovation. Argonne encourages everyone to apply for employment. Argonne is committed to nondiscrimination and considers all qualified applicants for employment without regard to any characteristic protected by law.

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.

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