Applications are invited for a postdoctoral position at the SUNCAT Center for Interface Science and Catalysis at SLAC National Accelerator Laboratory / Stanford University. The successful candidate will play an important role in developing machine learning models predicting the stability, activity, and selectivity of heterogeneous catalysts by combining experimental and computational data. A particular emphasis in the project will be the discovery of human-interpretable ML models that will enable developing an atomic-level understanding of catalytic processes under realistic experimental conditions.
As the successful candidate, you will have:
- A PhD in mathematics, computer science, physics, chemistry, materials science, or related field
- A strong background in machine learning, computational physics, or chemistry
- Proficiency in programming and data analysis with Python
- Excellent communication skills
- The ability to work independently and collaboratively in a diverse research team
The position is open now and it is initially for one year with possibility of extension for another year based on performance, mutual consent, and anticipated continued funding.
Preview of applications begins immediately.
Interested applicants are encouraged to apply via email to
Dr. Johannes Voss email@example.com
Please include a letter of motivation, C.V. with list of publications, and the contact information for two to three academic references.