The Center for Nanoscale Materials (https://www.anl.gov/cnm) at Argonne National Laboratory (Chicago, US) has an immediate opening for a Postdoctoral Appointee in the use of computational modeling and machine learning to interpret experimental x-ray, electron, and scanning probe characterization data, especially for energy materials. The postdoctoral researchers 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, especially x-ray spectroscopies. 

Position Requirements

• Recently received or near completion of their Ph.D. in Nanoscience or related discipline within the last 3 years.
• Density functional theory modeling of materials, e.g. high throughput calculations,  structure prediction, classical and ab initio MD simulations
• Simulation of experimental characterization signals, e.g. x-ray spectroscopy, electron microscopy, scanning tunneling microscopy, and neutron scattering
• Use of machine learning approaches 
• Development and implementation of algorithms and software for materials modeling
• High-performance and parallel computing
• Excellent communication and analytical skills
• Ability to work independently and in an interdisciplinary collaborative environment, in close collaboration with experimentalists.
• Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork.

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