Postdoctoral Appointee - Computational Chemistry

Argonne National Laboratory • Lemont, IL

10d • $72,879 - $121,465 • Onsite

About The Position

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.

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.

Nice To Haves

• Experience with density functional theory calculations or the development, training, validation, or application of machine learning interatomic potentials.
• Experience developing enhanced sampling methods.

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.

Benefits

• comprehensive benefits

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree