Applications are invited for a postdoctoral research fellow position at the SUNCAT Center for Interface Science and Catalysis at Stanford University/SLAC National Accelerator Laboratory. In this project, surrogate machine learning models will be developed replacing costly first-principles simulations for establishing microkinetic models of activity and selectivity in thermal heterogeneous catalysis. The activity will use surface reaction energies from SUNCAT’s extensive catalysis-hub.org database as a starting point and will employ active learning to guide the search for stable, active, and selective catalysts for selective oxidation reactions.

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.
- Excellent mathematical and analytical skills.
- Proficient English language skills both oral and written.
Desired but not required:
- Experience in simulating heterogeneous catalytic reactions using density functional theory.
- Experience in developing active machine learning models.

The starting date is negotiable with an earliest possible starting date of May 2022. The position is initially for one year with possibility of extension for another year based on performance, mutual consent, and programmatic need.
Preview of applications begins immediately, and applications are accepted until the position is filled.

Interested applicants are encouraged to apply via email to
Dr. Johannes Voss [email protected]
Please include a cover letter highlighting previous achievements relevant to this position and motivation, a C.V. with list of publications, and the contact information for two to three academic references.

Further information about the group:
https://web.stanford.edu/group/suncat/vossgroup/
https://suncat.stanford.edu/