The Predictive Materials Modeling group in Department of Mechanical Engineering at University of Louisville has immediate openings for 1-2 PhD students (in Fall 2020 or Spring 2021) in the area of modeling phase transitions in quantum perovskite oxides. The project is supervised by Prof. Badri Narayanan, and is suitable for students with a passion for solid-state physics, 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 advance fundamental understanding of defect dynamics, microstructural evolution, phase transitions, and electronic behavior of complex oxides for emerging applications in neuromorphic computing. The researchers on this project will have opportunity to collaborate extensively with experimental groups at Argonne National Laboratory, and University of Louisville.

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 physics.  Prospective students with passion for scientific programming, materials modeling, and machine learning are encouraged to apply.

The appointed candidate(s) will receive full-tuition coverage, stipend, and health insurance benefits.

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]).