The Theory and Modeling Group at the Center for Nanoscale Materials (CNM), Argonne National Laboratory (near Chicago, Illinois), invites applications for a postdoctoral appointment focused on theory and simulation of the electronic structure of polycrystalline and defective two-dimensional materials. Interviews will begin immediately and continue until the position is filled.

This position centers on developing machine-learning surrogates for electronic structure and electrostatic potential and using these models to predict structural and electronic evolution under applied bias. Methods may include density functional theory (DFT)-based approaches, coupled with effective continuum and/or tight-binding models derived from first-principles calculations.

The project is part of a large, multi-institution collaboration (~30 researchers) spanning Argonne National Laboratory, Oak Ridge National Laboratory, Northwestern University, and Lawrence Berkeley National Laboratory.

Key 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

Please submit a CV and a cover letter (max 1-2 page) that addresses the following three questions:

• What materials modeling technique(s) do you have expertise in?

• What is your experience with software development?

• What is your experience with collaborative projects with experimental groups?

Position Requirements

• 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 Qualifications

• 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

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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.