Research Associate in Materials Science and Engineering

University of Virginia

Charlottesville, VA

Job posting number: #7068799

Posted: August 12, 2020

Application Deadline: Open Until Filled

Job Description

Professor Balachandran’s Materials Informatics group in the Departments of Materials Science and Engineering & Mechanical and Aerospace Engineering at the University of Virginia is seeking to fill a Postdoctoral Research Associate position studying the computational design of high entropy alloys (HEAs) using density functional theory and artificial intelligence methods. The successful candidate, under general oversight of the principal investigator, will develop novel computational approaches to accelerate the search and discovery of novel HEAs.

Additional Responsibilities include: performing collaborative research and participating in mentoring students as well as helping draft proposals, reports, and research papers.

QUALIFICATIONS: Candidates must have a PhD in Materials Science, Chemical Engineering, Physics or a related field and be familiar with most or all of the following research areas: Alloy Design, Calculation of Elastic, Phonon and Thermal properties, Special Quasirandom Structures method. Familiarity in ML codes such as Python and R is essential. Good oral and written communication skills, as well as attention to detail, are essential.

APPLICATION PROCEDURE: Apply online at https://uva.wd1.myworkdayjobs.com/UVAJobs and attach a cover letter indicating research interests, a detailed curriculum vitae, and contact information for three references. Please note that multiple documents can be uploaded in the CV box. Make sure to upload all the documents to complete your application.

APPLICATION DEADLINE: Review of applications will begin on August 23, 2020 and the posting will remain open until filled. The University will perform background checks on all new hires prior to employment.

This is a one-year appointment; however, the appointment may be renewed for an additional year contingent upon available funding and satisfactory performance.





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