University of Southern California

How this program compares

At an estimated $74.6K in total tuition, the Online MS in Materials Engineering - Machine Learning sits about 76% above the $42.4K average for AI master's programs in our database — placing it in the 88th percentile on cost among the 531 we track at this level. It is one of the 57% of programs in our database offered fully or partly online.

Admission Snapshot

Typical admitted student: Applicants typically need a bachelor's degree in materials engineering, mechanical engineering, chemistry, physics, or related field with a strong quantitative background. Prior coursework in programming and mathematics (linear algebra, calculus, differential equations) is strongly preferred.

About This Program

This program combines materials engineering with machine learning to prepare students for data-driven materials discovery and cybermanufacturing. Coursework concentrates on Machine Learning and Deep Learning. Most students complete it in about 2 years.

Estimated total tuition is $74.6K, above the $42.4K average for AI master's programs in our database and in the 88th percentile on cost at this level. Applicants should weigh that premium against the program's outcomes and brand.

Position as Machine Learning Engineer in Engineering AI with machine learning expertise Graduates frequently move into roles such as Machine Learning Engineer, with reported salaries around $98,000.

Career Outcomes

Position as Machine Learning Engineer in Engineering AI with machine learning expertise

• 1. Materials Data Scientist
• 2. Computational Materials Engineer
• 3. AI/ML Research Scientist (Materials Industries)
• 4. Product Development Engineer (Advanced Materials)

What You'll Learn

• Design and implement machine learning models to predict material properties and optimize material composition
• Apply deep learning techniques to materials characterization and image analysis
• Develop computational methods for accelerated materials discovery and development
• Integrate domain expertise in materials engineering with advanced data science and AI methodologies

Curriculum Highlights

Core requirements include basics of machine learning for materials, mathematical methods for deep learning, and atomistic simulation of materials.

Top Employers

Top employers include aerospace companies like Boeing and Lockheed Martin, automotive firms like Tesla and General Motors, semiconductor manufacturers like Intel and TSMC, and advanced materials companies like DuPont and 3M.

Admissions

Applicants typically need a bachelor's degree in materials engineering, mechanical engineering, chemistry, physics, or related field with a strong quantitative background. Prior coursework in programming and mathematics (linear algebra, calculus, differential equations) is strongly preferred.

Application Materials

• Statement of Purpose: Required
• Letters of Recommendation: 3
• Resume: Required
• Transcripts: Official transcripts required

Academic Requirements

• Degree Required: MS (Master of Science)
• GRE/GMAT: Required
• TOEFL/IELTS: Required for international students (TOEFL 80+ / IELTS 6.5+)