Postdoctoral Appointee - Computational Quantum Chemistry and Machine Learning

The Molecular Materials Group in the Materials Science Division (MSD) of Argonne National Laboratory is seeking applicants for a postdoctoral fellow appointment in the area of Computational Quantum Chemistry and Machine Learning.

The research project(s) aims at understanding discovering optimal molecules for hydrogen storage and carbon-free combustion energy storage applications. This Computational project involves atomistic modeling (Gaussian, NWCHEM), molecular dynamics, data analysis and machine learning.

This position will involve a considerable number of computational simulations using atomistic modeling of molecules for predicting thermodynamics and kinetics. The candidate is expected to think and perform simulations independently and conduct research in a highly interdisciplinary environment of chemists, material scientists, and computer scientists. The candidate is expected to publish and present, project reports, and peer reviewed papers and proceedings.

Position Requirements

Knowledge, Skills and Experience:
  • Primary major in Computational quantum chemistry, in particular molecular chemistry, reactivity, structure-property prediction.
  • A recent (within past three years) PhD in Chemistry or Chemical Engineering/Materials Science is essential.
  • A strong knowledge in simulating reaction mechanisms (using Gaussian/NWChem and VASP) is desired.
  • Demonstrated computational chemistry theoretical knowledge via lead publications is required.
  • Knowledge of using high performance computing facilities, machine learning and data science techniques (Scikit Learn, Tensorflow/PyTorch), programming (python), and scripting is useful.

Job Family
Postdoctoral Family

Job Profile
Postdoctoral Appointee

Worker Type
Long-Term (Fixed Term)

Time Type
Full time

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