RESEARCH ASST I - (Student/Work Study)

About the role

Work with Dr. Bin Wang on developing machine learning algorithms to study material structures, properties, and applications. May involve experimental work. The position is hybrid, allowing both onsite and offsite work with an expected recurring onsite presence; additional onsite presence may be required as needed. Work agreements are reviewed annually and subject to change.

Responsibilities

• 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).

Qualifications

Required: 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.

• Desired: Coding skills
• Desired: Chemistry and materials skills