Research ASST Student​/Study

Job Summary

Work with Dr. Bin Wang on developing machine learning algorithms to study material structures, properties, and applications. May involve experimental work.

• Curate and analyze datasets of super ionic conductors' structures and properties.
• Build and train machine learning models (especially graph neural networks) to predict materials properties (e.g., ionic conductivity, stability).
• Communicate and present findings in team meetings, research fairs, and conferences (with support from the professor).

• Must be a UM-Flint Undergraduate student.
• Have taken or are currently enrolled in CHM 260 (General Chemistry I).
• Willingness to learn, or have taken/are currently enrolled in, an introductory programming or data science course.

• Coding skills.
• Chemistry and materials skills.

Hybrid. The work requirements allow both onsite and offsite work and an employee has an expected recurring onsite presence. On occasion, the employee may be required and must be available to work onsite more frequently if necessitated by unit leadership or their designee and/or the job requirements.

The University of Michigan conducts background checks on all job candidates upon acceptance of a contingent offer and may use a third party administrator to conduct background checks. Background checks are performed in compliance with the Fair Credit Reporting Act.

Job openings are posted for a minimum of three calendar days. The review and selection process may begin as early as the fourth day after posting. This opening may be removed from posting boards and filled any time after the minimum posting period has ended.

The University of Michigan is an Equal Opportunity Employer. We are committed to providing an environment of mutual respect where equal employment opportunities are available to all applicants, including protected veterans and individuals with disabilities.