SUMMARY: A Postdoctoral position is available for immediate start to develop density functional theory (DFT) and machine learning (ML) based methods to model the stability and catalytic activity of dynamic transition metal oxide surfaces. We are seeking a highly qualified candidate with a strong background in heterogeneous (electro) catalysis, density functional theory and machine learning. Experience with global optimization algorithms, molecular dynamics and/or ML force fields also desirable.
KEYWORDS: Catalysis data science, surface dynamics, machine learning
DESCRIPTION: The goal of the project is to develop DFT and ML based methodology to efficiently sample the (meta) stable surface morphologies of transition metal oxide catalysts. Structures of interest include defective, amorphous and mixed-composition oxides that are generally subject to surface restructuring during electrocatalytic conditions. The methodology will involve molecular dynamics simulations (using DFT and ML force fields) combined with global optimization algorithms to obtain an efficient sampling of surface structures.
The research is funded by the US Department of Energy through our core SUNCAT-FWP funding, with the possibility of continuation based on a yearly evaluation.
A successful candidate will satisfy the following:
- A PhD in chemical engineering, materials science, physics, chemistry or related field
- Strong background in modeling surfaces with density functional theory, preferably with experience with global optimization algorithms and/or force-field models.
- Machine learning experience as well as a strong proficiency in Python programming and data analysis.
- Excellent communication skills and a career level-appropriate publication track record.
- The ability to work independently and collaboratively in a diverse research team.
Preview of applications begins immediately. Applications are accepted until the position is filled.
Please include a letter of motivation, C.V. with list of publications, coding contributions (such as git repository), and the contact information for two to three academic references.
CONTACTS: Please send application to Kirsten Winther (winther@stanford.edu) and Michal Bajdich (bajdich@stanford.edu) or contact us for further details on the position.