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This postdoctoral position involves the modelling of molten salts and ionic liquids related to sustainable steel production studies at Argonne National Laboratory.

The focus of this position is on developing an integrated computational framework that harnesses simulations and experimental data to create models for providing rapid feedback on experiments related to determination of iron speciation in intermediate-temperature ionic liquids (including but not limited to molten inorganic halide salts) and using the insights to find optimal conditions for electrodeposition of transition metals. The successful candidate will have the opportunity to develop, use, and deploy state-of-the-art tools on the largest supercomputers used for scientific computing.

The work is part of a larger collaboration between Argonne National Laboratory, Oak Ridge National Laboratory, the University of Illinois, and other institutions.

Position Requirements

• A recent Ph.D. in physics, chemistry, materials science, or a related field is required.

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

Demonstrated expertise in the following topics:

• Atomistic modelling (density functional theory, ab initio or classical molecular dynamics) of metal-inorganic complexes and clusters.

• Scientific programming (e.g. C++, Fortran, python,….)

• An understanding of basic concepts in deep learning and PyTorch will be a plus.

Preferred skills and qualifications:

• Understanding of electrochemistry.

• Experience modelling ionic solutions or molten salts.

• Experience developing machine learning force fields.

• Experience using high-performance computing platforms.

• Effective collaborative and communication skills.

Job Family

Postdoctoral Family

Job Profile

Postdoctoral Appointee

Worker Type

Long-Term (Fixed Term)

Time Type

Full time

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