Argonne National Laboratory · 11 hours ago
Postdoctoral Appointee – ML-Accelerated Electronic Structure Modeling of 2D Materials (CNM)
Argonne National Laboratory is seeking a Postdoctoral Appointee focused on ML-accelerated electronic structure modeling of 2D materials. The role involves developing machine-learning surrogates for electronic structure, performing large-scale simulations, and collaborating with experimental teams to advance materials research.
EnergySecuritySocial Impact
Responsibilities
Develop and validate ML surrogate models for electronic structure and electrostatic potential in 2D materials
Perform large-scale materials simulations (e.g., DFT, tight-binding, continuum models) to generate training and validation datasets
Integrate surrogate models into workflows to predict bias-driven structural and electronic evolution
Design and execute high-throughput calculations; build and manage curated materials databases
Collaborate closely with experimental teams to inform model development and interpret results
Contribute to software development, documentation, and reproducible workflows
Disseminate findings through publications, presentations, and collaborative reports
Qualification
Required
Recent or soon-to-be-completed PhD (within the last 0-5 years) in field of physics, chemistry, materials science, electrical engineering, or a related field
Demonstrated expertise in electronic structure theory
Experience with large-scale materials simulations
Experience developing and applying machine-learning surrogates for atomistic simulations
Excellent verbal and written communication skills
Strong collaborative skills and the ability to work effectively across divisions, laboratories, universities, and industry
Ability to model Argonne's core values of impact, safety, respect, integrity, and teamwork
Preferred
Hands-on experience with two-dimensional materials modeling
Proficiency in database development and management for computational materials data
Strong programming skills and experience with software development best practices
Experience with high-throughput calculations and workflow automation
Familiarity with inverse design approaches
Benefits
Comprehensive benefits are part of the total rewards package
Company
Argonne National Laboratory
Argonne National Laboratory conducts researches in basic science, energy resources, and environmental management.
Funding
Current Stage
Late StageTotal Funding
$41.4MKey Investors
Advanced Research Projects Agency for HealthUS Department of EnergyU.S. Department of Homeland Security
2024-11-14Grant· $21.7M
2023-09-27Grant
2023-01-17Grant
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