Argonne National Laboratory’s Materials Science Division (MSD) invites applications for a Postdoctoral Appointee in computational materials chemistry and atomistic modeling to investigate the behavior of soft materials under operating conditions.

The successful candidate will implement state of the art molecular dynamic simulations using machine learning potentials and apply free energy methods to uncover mechanisms of chemical reactivity and degradation. The appointee will have the opportunity to collaborate with a multidisciplinary team, working closely with experimental researchers whose studies will complement and inform the computational findings.

Responsibilities include designing and performing simulations, developing analysis workflows, interpreting results, and preparing scientific publications and presentations.

Key Responsibilities

- Perform nonequilibrium molecular dynamics (NEMD) simulations using conventional force fields and machine learning potentials, including foundation models.

- Apply free energy and enhanced sampling methods to study molecular systems and support model training.

- Collaborate with a multidisciplinary team to connect computational findings with complementary experimental studies.

- Communicate research results through publications, presentations, and team discussions.

This position description documents the general nature and level of work but is not intended to be a comprehensive list of all activities, duties and responsibilities required of job incumbent. Consequently, job incumbent may be required to perform other duties as assigned.

Position Requirements

- Recent or soon-to-be-completed PhD (typically completed within the last 0-5 years) in chemistry, materials science, physics, chemical engineering, or a closely related field, completed by the appointment start date.

- Demonstrated experience designing, performing, and analyzing classical or ab initio molecular dynamics simulations.

- Knowledge of free energy methods and experience applying them to molecular systems.

- Familiarity with scientific programming and data analysis using languages such as Python or Julia, and experience with Linux and high-performance computing environments.

- Strong written and oral communication skills, with the ability to work independently and collaboratively.

- Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork.

- This position requires on-site presence at the Argonne campus in Lemont, Illinois, 5 days per week.

Preferred Qualifications:

- Experience with density functional theory calculations or the development, training, validation, or application of machine learning interatomic potentials.

- Experience developing enhanced sampling methods.

Please upload the following as attachments to your application via Workday:

• CV/Resume

• Unofficial Ph.D. Transcripts

• If already awarded, a copy of your Ph.D. diploma. Candidates can be currently enrolled to apply, but must have their degree conferred by the position start date

Job Family

Postdoctoral

Job Profile

Postdoctoral Appointee

Worker Type

Long-Term (Fixed Term)

Time Type

Full time

The 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.