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The Center for Nanoscale Materials (https://www.anl.gov/cnm) at Argonne National Laboratory (Chicago, US) has an immediate postdoctoral opening in the use of computational modeling and machine learning to interpret experimental x-ray and electron characterization data. The postdoctoral researcher will work within the group of Dr Maria Chan (https://www.anl.gov/profile/maria-k-chan) on developing and applying algorithms and software for the use of first principles and atomistic modeling, together with machine learning, to accelerate the inversion of experimental characterization data. The postdoctoral researcher will be collaborating extensively with researchers at Argonne, other national laboratories, as well as universities.

Position Requirements

• A PhD in computational physics, chemistry, materials science, chemical engineering, or a related field is required.

• Excellent communication and analytical skills are required

• Software programming experience is necessary

• Ability to work independently and in an interdisciplinary collaborative environment, in close collaboration with experimentalists, is expected.

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

Applicants should have expertise in some of the following:

• Density functional theory/molecular dynamics modeling of materials, e.g. high throughput calculations, structure prediction, classical and ab initio MD simulations

• Simulation of experimental characterization signals, especially core-level spectroscopy

• Use of machine learning approaches to analyze spectroscopy data

• Development and implementation of algorithms and software for materials modeling

• High-performance and parallel computing

Job Family

Postdoctoral Family

Job Profile

Postdoctoral Appointee

Worker Type

Long-Term (Fixed Term)

Time Type

Full time

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