- Program Overview
- Application Information
- Tuition & Fees
- Career Outcomes
- DEN@Viterbi - Online Delivery
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As of September 7, 2021, the GRE exam is not required.
The Master of Science in Materials Engineering with an emphasis in Machine Learning is for students who have an interest in materials engineering that includes machine learning toward materials discovery, design, and processing. U.S. industry and cybermanufacturing are rapidly moving toward data-driven materials discovery and development. Materials engineering combined with machine learning is a novel emerging field that combines materials modeling, simulations and machine learning together into a new paradigm for materials discovery and cybermanufacturing.
Students with a Bachelor of Science in Materials Science, Chemical Engineering, Mechanical Engineering, Civil or Environmental Engineering, Industrial Engineering, Physics, and Chemistry, as well as in industry employees who plan to apply machine learning to their research and development, are ideal candidates for the program.
This degree is awarded in conformity with the general requirements of the Viterbi School of Engineering. Students may elect to work for this degree in either the Materials Science or Aerospace and Mechanical Engineering departments. The specific courses that constitute an acceptable program must be approved in advance by the administering department.
- This program takes 1.5 - 2 years to complete
- A thesis option is available for this program
- Chat with a USC Viterbi Graduate Student Ambassador
Visit our Profiles Page to learn more about our students and alumni
Please Note: Requirements for graduation, course offerings, course availability, track offerings and any other degree requirements are subject to change. Students should consult with an academic advisor prior to registering for any classes.
- A minimum of 28 units is required to earn the MS in Materials Engineering - Machine Learning degree
- A minimum cumulative GPA of 3.0 is required for graduation
MATERIALS ENGINEERING (MACHINE LEARNING) WEBINAR
Hosted by faculty members involved in this area, Andrea Hodge, Priya Vashista, and Ken-ichi Nomura. Do not miss your chance to ask these professors questions about the program and the future of this exciting new area.
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