Research Assistant I: ML for Materials (GNNs, Hybrid)

University of Michigan; Flint (MI); Hybrid; USD 17,000 - 22,000; Part time

Job summary

The University of Michigan is seeking an undergraduate student from UM-Flint to collaborate with Dr. Bin Wang on developing machine learning algorithms for studying material structures, properties, and potential applications.

The role may include experimental work and teamwork across disciplines. You will curate datasets of superionic conductors and help build graph neural networks to predict ionic conductivity and stability, with opportunities to present at meetings and research fairs alongside

Qualifications

• 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.

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).

Skills

Coding skills

Chemistry & materials skills

Education

Undergraduate student at UM-Flint

Job description

The University of Michigan is seeking an undergraduate student from UM-Flint to collaborate with Dr. Bin Wang on developing machine learning algorithms for studying material structures, properties, and potential applications.

The role may include experimental work and teamwork across disciplines. You will curate datasets of superionic conductors and help build graph neural networks to predict ionic conductivity and stability, with opportunities to present at meetings and research fairs alongside