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

Under the guidance of the professor, students will contribute to:

• Curate and analyze datasets of superionic 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 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.

Positions that are eligible for hybrid or mobile/remote work mode are at the discretion of the hiring department. Work agreements are reviewed annually at a minimum and are subject to change at any time, and for any reason, throughout the course of employment. Learn more about the work modes.

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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.

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