A postdoctoral position is available in the Wen Group in the William A. Brookshire Department of Chemical & Biomolecular Engineering at the University of Houston. The Wen Group leverages artificial intelligence, high-performance computing, and atomistic molecular simulations to understand and design new materials for renewable energy and healthcare applications.

Depending on the interest and experience, the successful candidate is expected to work on one of the two topics:

1. Data-driven modeling and understanding of battery materials: developing machine learning interatomic potentials and applying them in classical molecular simulations to study the structural, mechanical, and thermal properties. (Collaboration opportunities with the OpenKIM and ColabFit projects)
2. Machine learning in the chemical reaction space: performing first-principles calculations and building machine learning models to understand and design catalysts such as zeolites. (Collaboration opportunities with the Materials Project)

Requirement

The position requires:

• A recent Ph.D. in Materials Science & Engineering, Chemical Engineering, Physics, Chemistry, Mechanical Engineering, and related fields
• Practical knowledge of density-functional theory calculations or classical molecular simulations
• Basic understanding of thermodynamics and kinetics
• Experience in computer programming and software development, preferably in Python

Experience in the following areas is a plus:

• Basic understanding of machine learning and familiarity with machine learning frameworks (e.g., PyTorch, Scikit-learn, Jax, and TensorFlow)
• Database (e.g., MongoDB) usage and administration
• Experience with automating workflows and DevOps tools (e.g. git)

Application

Please send an email to Dr. Mingjian Wen ([email protected]) with the below info:

• Expected start date
• Which of the two topics you are interested in
• An up-to-date CV/resume