SUMMARY: A Post Doctoral position is available for immediate start in catalysis data science. We are seeking a highly qualified candidate that possesses a detailed understanding of density functional theory and machine learning. Experience with surface calculations, high throughput computation, and electrocatalysis is also desirable.
KEYWORDS: Catalysis data science, DFT, machine learning
DESCRIPTION: The project focuses on developing machine learning accelerated frameworks for the discovery of mixed and doped transition metal oxide catalysts. The work will involve the development ML models for adsorption energetics based on electronic structure descriptors, as well as integrating these models with active learning ML frameworks. Furthermore, the candidate is expected to collaborate on projects involving fundamental and electrochemical aspects. The research is funded through DOE and the position is for 2 years (1+1), where continuation is based on a yearly evaluation.
A successful candidate will satisfy the following:
- A PhD in physics, chemical engineering, materials science, or related field
- A strong understanding of the electronic structure of materials (experience with modeling surfaces with density functional theory is desireable).
- Proficiency in programming and data analysis in Python as well as experience with machine learning and/or high throughput computation.
- 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 (firstname.lastname@example.org) or contact for further details on the position.