Applications are invited for a postdoctoral research position at the SUNCAT Center for Interface Science and Catalysis in applying machine learning techniques to improve the prediction of surface reaction energetics.
The activity will consist of
a) using machine learning to construct new improved multi-purpose meta-exchange-correlation functionals supplemented with non-local correlation terms
b) using genetic programming to find post-DFT corrections for better models for surface reactions in heterogeneous catalysis.

Requirements:
- A Ph.D. in physics, chemistry, materials science, or a related field.
- A strong background in computational physics or chemistry.
- Proficiency in scientific programming using Python (Fortran and C++ skills are a plus but not necessarily required).
- Experience in routinely performing planewave DFT calculations and modeling surface reactions.
- Excellent mathematical and analytical skills.
- Proficient English language skills both oral and written.

The earliest possible starting date is June 2020, and the position is initially for one year with possibility of extension for another year based on mutual consent and continued funding.

Interested applicants are encouraged to apply via email to
Dr. Johannes Voss [email protected]
Please include a letter of motivation, C.V. with list of publications, and the contact information for two to three academic references.
Preview of applications begins immediately. Applications are accepted until the position is filled.

The SUNCAT Center for Interface Science and Catalysis is a partnership between the Stanford School of Engineering and SLAC National Accelerator Laboratory. The Center explores challenges associated with the atomic-scale design of catalysts for chemical transformations of interest for energy conversion and storage.

For further information:
Dr. Johannes Voss [email protected]
https://web.stanford.edu/group/suncat/vossgroup/