The Predictive Materials Modeling group (led by Prof. Badri Narayanan) in Department of Mechanical Engineering at University of Louisville has an immediate opening for a Postdoctoral position in the area of modeling phase transitions in quantum perovskite oxides. The appointment is for three years, subject to satisfactory research performance and continued availability of funding. Salary will be commensurate with experience, training and qualifications.  

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.

Successful candidates will have demonstrated expertise in two or more of the following areas: Density Functional Theory, Ab initio Molecular Dynamics, Reactive Molecular Dynamics, Dynamical Mean Field Theory, Advanced Sampling methods, Machine Learning, and optimization methods.  Excellent programming abilities (preferably in Python), analytical skills, and knowledge of high performance computing are also required. Experience with high-throughput computing, machine-learning methods, and big data analytics will be an added advantage. The project involves strong ties with experimental groups; so, a proven track record of collaborations with synthesis and characterization groups will be advantageous.

A PhD in Materials Science, Chemical Engineering, Mechanical Engineering, Chemistry, Physics or a related field is required. Applicants must have received their Ph.D within the last 5 years. Review of applications will begin immediately and continue until the position is filled.

Interested candidates should send their (a) CV containing a list of publications, (b) a cover letter describing their background, key research accomplishments, and capability pertaining to modeling strongly correlated complex oxides, and (c) names, email, and telephone numbers of three references by email to Prof. Badri Narayanan ([email protected]).