The Predictive Materials Modeling group in Department of Mechanical Engineering at University of Louisville has an opening for a PhD studentship (beginning Spring 2020) in the area of computational materials science. The project is supervised by Prof. Badri Narayanan, and is suitable for students with a passion for solid-state chemistry, materials science, programming and computational modeling, big-data analysis and machine learning.

The project involves use of material modeling (at atomic-to-mesoscopic length/time-scales) and machine learning to (a) advance fundamental understanding of reaction chemistry, kinetics, charge transfer, and dendrite growth; and (b) enable predictive design of electrode/electrolyte interfaces for high-performance solid-state lithium-sulfur batteries. The researchers on this project will have opportunity to collaborate extensively with experimental groups at University of Louisville, as well as several national laboratories (Argonne, Oak Ridge, and Army Research).

Applicants should hold a Bachelors or Masters degree in Materials/Metallurgical Engineering, Mechanical Engineering or Chemical Engineering; and have a strong background in materials science, and solid-state chemistry.  Prospective students with passion for scientific programming, materials modeling, and machine learning are encouraged to apply.

Interested candidates should send their CV, statement of research interests, standardized test scores (GRE / TOEFL) and names of three references by email to Prof. Badri Narayanan ([email protected]).