Graduate degree programs
Our graduate programs prepare you for the future of artificial intelligence, manufacturing, systems engineering and robotics.
Master’s degree programs
Artificial intelligence engineering (manufacturing), MS
This program integrates AI engineering with advanced manufacturing, preparing students to apply machine learning, computer vision and natural language processing. Graduates will learn AI engineering foundations, data collection and AI ethics and social responsibility.
Artificial intelligence engineering (robotics), MS
This program blends advanced AI and robotics engineering, preparing students to apply machine learning, automation and generative AI in real-world systems. Graduates gain in-demand skills for roles in robotics, AI and industrial innovation.
Manufacturing engineering, MS
This program prepares students to lead in smart manufacturing, combining automation, materials processing and systems management. Graduates gain advanced skills to design efficient, high-tech production systems and excel in roles across diverse industries.
Robotics and autonomous systems (systems engineering), MS
This program equips students to design and manage complex robotic and autonomous systems. With a focus on systems engineering, AI and automation, graduates are prepared for advanced roles in industries from aerospace to healthcare.
Doctoral degree programs
Manufacturing engineering, PhD
This program prepares students to lead advanced research in global manufacturing. With hands-on training and industry collaboration, graduates pursue careers in academia, R&D and innovation across national labs, companies and research institutes.
Robotics and autonomous systems (mechatronics and automation), PhD
This program focuses on areas in mechatronics and automation within robotics and autonomous systems. Graduates will become expert researchers equipped for impactful careers in academia, industry R&D and national laboratories across advanced engineering fields.
Systems engineering, PhD
This program trains students to design and manage complex, multidisciplinary systems using advanced modeling and analysis. Graduates lead systems integration efforts in industry and government, driving innovation in engineering and interdisciplinary problem-solving.
Curriculum overview: Manufacturing engineering, MS
The 30 credit hours must include 3 required core courses, or 9 credit hours, and 4 courses, or 12 credit hours, from a focus area of interest. The remaining courses can be taken from approved technical electives that span the course designation MFG or RAS. Other Engineering courses are also acceptable but verify your selection with the advising office before enrolling.
Following are highlights of the M.S in Manufacturing Engineering curriculum:
- Prepares you with a core understanding of Applied Linear Algebra, Industrial Statistics and Manufacturing Systems Management for advanced manufacturing operations.
- One of three focus areas for the student to focus their learning: Advanced Manufacturing Processes, Factory Operations Management and Smart Manufacturing
- Allows students to pursue a portfolio of courses to complete their Master’s degree.
- Or pursue the option of an applied technical project or a thesis level work in a focus area of interest.
Manufacturing engineers collaborate on interdisciplinary teams to design, manufacture and deliver innovative technological products and services. The graduate program enables students to develop not only sophisticated engineering technical skills but also the important professional skills of communication, teamwork and collaboration, and the adaptability that many employers seek.
- RAS 501 Applied Linear Algebra OR EGR 520 Engineering Analysis
- MFG 510 Manufacturing Systems Management
- EGR 522 Statistics for Quality Control in Manufacturing
This focus area emphasizes manufacturing processes that are typically carried out in production floors to manufacture products. Particularly suited to those who are interested in core design and scalable manufacturing processes, with emphasis on industrial metrology.
- MFG 546 Nondestructive Testing
- MFG 573 Micro/Nano Additive Manufacturing
- MFG 598 Design for Additive Manufacturing
- MFG 598 Integrated Circuit Manufacturing
- MFG 598 Scalable Nanomanufacturing
Pick any four: Not all courses are offered every semester. See Advising for assistance.
This focus area emphasizes sustainable and removal of wasteful practices in factory floor operations with a blend of statistics and lean management. Particularly suited to those who have a penchant for lean engineering management within factory floors.
- MFG 581/EGR 581 Simulating Manufacturing Systems
- OMT 570 Advanced Project Management
- IEE 561 Production Systems*
- IEE 572 Design of Experiments*
Pick any 4: Not all courses are offered every semester. See Advising for assistance.
This focus area emphasizes integration of computing, data science and information technology in manufacturing operations. Particularly suited to those who have interests at the intersection of manufacturing and information technology.
- MFG 523 Artificial Intelligence for Smart Manufacturing
- MFG 524 Engineering Computing with Python
- MFG 598 Industrial Internet of Things
- MFG 581 Simulating Manufacturing Systems
- RAS 585 Machine Learning and Artificial Intelligence
- IEE 572 Design of Experiments
Pick any 4: Not all courses are offered every semester. See Advising for assistance.
Acceptable electives include 500-level courses with a prefix of MFG, RAS. Other courses may be acceptable; verify your selection with the advising office before enrolling.
Elective credits vary based on student selection of culminating experience. See program handbook or Advising for details.
Select one culminating experience:
- Portfolio (0 credits)
- MFG 593 Applied Project (3 credits)
- MFG 599 Thesis (6 credits)
See program handbook or Advising for details.
Curriculum overview: Robotics and autonomous systems (systems engineering), MS
The 30 credit hours must include 2 required core courses (6 credit hours) and 2 concentration courses (6 credit hours) from a focus area of interest. The remaining courses can be taken in the form of electives, in which students can choose from the approved elective list.
- RAS 501 Applied Linear Algebra for Engineers
- RAS 545 Robotic Systems I
RAS 550 Mechatronic Systems and one of the following:
- RAS 546 Robotic Systems II
- RAS 557 Foldable Robotics
- RAS 585 Machine Learning and AI
- RAS 598 Experimentation and Deployment of Robotic Systems
- EGR 520 Engineering Analysis I
- RAS 555 Mechatronics Device Innovation
- EGR 560 Vehicle Dynamics and Control
- RAS 556 Topic: System Control and Optimization
- EGR 598 Topic: Zero Emissions Vehicles
- RAS 598 Topic: Transforms and Systems Modeling
- EGR 598 Topic: Mechanical Engineering Systems
- EGR 598 Topic: Power Electronic Converters and Systems
- EGR 608 Adv Simulation
- EGR 611 Complex Engineering Systems
- IEE 576 Network Optimization and Algorithms
- SER 594 Software Engineering for Machine Learning
- SES 598 Autonomous Exploration Systems
Courses not counted as concentration classes can be taken as electives that fall in emphasis areas. Students can take any of the concentration courses listed in the handbook as electives, or select electives from this list. Be sure to check the handbook and the linked elective list often for updates.
First elective:
- RAS 546 Robotic Systems II
- RAS 555 Mechatronics Device Innovation
- RAS 557 Foldable Robotics
- RAS 598 Experimentation & Deployment of Robotic Systems
- MFG 598 Additive Manufacturing
- MFG 573 Micro/Nano Additive Manufacturing
- MFG 598 Multiphysics Digital Twins for Adv Mfg Processes
- AMT 570 Unmanned Aerial Systems
- SES 598 Autonomous Exploration Systems
Second elective:
- MFG 524 Engineering Computing with Python
- MFG 598 CNC Computer Programming
- MFG 598 Programming of Industrial Robotics System
- RAS 556 System Control and Optimization
- EGR 611 Complex Engineering Systems
- EEE 587 Optimal Control
- EEE 591 Foundations of Machine Learning: from Theory to Practice
Third elective:
- RAS 557 Foldable Robotics
- RAS 585 Machine Learning and AI
- MFG 524 Engineering Computing with Python
- MFG 598 Industrial Ultrasonic Testing
- MFG 598 AI in Additive Manufacturing
- MFG 598 Nondestructive Testing
- APM 598 Introduction to Deep Neural Networks
- EEE 598 Machine Learning
- MAE 551 Applied Machine Learning for Mechanical and Aerospace Engineers
Select one culminating experience:
- Portfolio
- RAS593 Applied Project (3)
- RAS599 Thesis (6)