Argonne National Laboratory seeks a Postdoctoral Appointee to perform computational research on materials for thermal and electrochemical interfaces. The successful candidate will integrate first-principles and atomistic simulations with machine-learned interatomic potentials to:
- Model reaction pathways on metal-oxide surface, including adsorption, reactions and diffusion steps.
- Construct atomistic structural models and compute electronic and vibrational properties.
- Develop and train neural-network or other machine-learned interatomic potentials to enable large-scale molecular dynamics (MD) simulations with near-DFT fidelity.
- Perform MD simulations to quantify thermal transport across interfaces and evaluate phonon coupling/phonon scattering mechanisms.
- Derive design rules relating synthesis to thermal and chemical properties; provide computational guidance to experimental synthesis.
- Collaborate closely with experimental teams to validate and refine models.
- Publish results in high-impact journals, present at conferences, and contribute to data sets and code repositories.
Position Requirements
Required Skills, Knowledge and Experience:
- Ph.D. (received within the last 0–5 years or by start date) in Physics, Materials Science, Chemistry, Chemical Engineering, Applied Physics, or a closely related field with a focus on computational materials modeling.
- Density Functional Theory (DFT) for surfaces and interfaces; experience computing reaction energies/barriers (e.g., NEB).
- Proficiency with one or more DFT packages (e.g., VASP) and phonon tools (e.g., Phonopy).
- Classical/ab initio Molecular Dynamics (MD) simulations of thermal transport; familiarity with NEMD and/or Green–Kubo approaches using LAMMPS or similar.
- Experience developing and applying machine-learned interatomic potentials and validating them against DFT.
- Atomistic modeling of reactions on metal oxides, including electrochemical reactions.
- Understanding of interfacial thermal transport, thermal boundary conductance, and phonon coupling across interfaces.
- Programming and HPC:
- Strong scripting and data analysis skills; experience with high-performance computing environments and job schedulers.
- Demonstrated ability to work in multidisciplinary teams and to communicate complex results effectively in writing and presentations.
- Record of peer-reviewed publications commensurate with career stage.
- Commitment to Argonne’s core values and safe work practices; ability to pass a DOE background check per lab requirements.
Preferred Skills, but Not Required:
- Background in integration of computational results with experimental data.
- Experience with workflow automation and reproducible research practices.
- Experience with machine learning in computational materials.
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 proof of degree conferral by the position start date.
At the point of interview, candidates will be asked to submit the name/contact of three references.
Job Family
Postdoctoral
Job Profile
Postdoctoral Appointee
Worker Type
Long-Term (Fixed Term)
Time Type
Full timeThe expected hiring range for this position is $70,758.00-$117,925.00.
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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