The M.S. in Civil & Computer Engineering combines the expertise of CMU’s Departments of Electrical & Computer Engineering (ECE) and Civil & Environmental Engineering (CEE) into one program. This interdisciplinary degree offers the opportunity for students to learn increasingly sought-after skills like designing, implementing, and managing smart infrastructure systems.
As the built environment becomes more connected, intelligent infrastructure systems collect data through sensors and advanced technologies, enabling more efficient, sustainable, and resilient systems. This joint degree program emphasizes not only the underlying technology and theory supporting these systems, but also the broader applicability and integration of control within the built and natural environments. Through that, it opens a wide range of possible areas that students can work at and make a positive impact.
By enrolling in this program, students will gain hands-on experience in the design and control of critical systems, such as mobility, energy, and water infrastructure, while addressing emerging threats and challenges like cyberattacks, extreme weather events, and urbanization. With courses in sensing, modeling, and control systems across multiple departments, students will graduate ready to lead in the development of safe, resilient, and human-centric systems.
Learn more about tuition costs and support.
Program requirements
To graduate with your M.S. in Civil and Computer Engineering, students must take a total of 108 units. At least 36 units must come from both ECE and CEE departments with six required courses: two in sensing, two in modeling, and two in controls.
Minimum of 108 units over 3 full time semesters, as described here:
- Minimum 36 units from Civil and Environmental Engineering (CEE)
- Minimum 36 units from Electrical and Computer Engineering (ECE)
- 24 units of graduate technical electives
- 12 units of free elective
- At least two courses from each of the following areas from CEE or ECE
- Sensing
- Modeling
- Controls
Reccommended courses
Sensing
12-711: BIM for Engineering, Construction, and Facility Management
12-750: Infrastructure Management
12-770: Autonomous Sustainable Buildings—Theory to Practice
12-778: Sensors, Circuits, and Data Interpretation and Management for Infrastructure Systems
12-830: Principles of Digital Twins
12-831: Introduction to AI for Predictive Analytics
18-648: Embedded Real-Time Systems
18-748: Networked Cyber-Physical Systems
18-614: MEME
Modeling
12-704: Probability and Estimation Methods for Engineering Systems
12-711: BIM for Engineering, Construction, and Facility Management
12-735: Urban Systems Modeling
12-750: Infrastructure Management
12-770: Autonomous Sustainable Buildings—Theory to Practice
12-774: Foundations of Intelligent Infrastructure Systems
12-644: Intro to Transportation Systems Analysis (Mini A)
12-830: Principles of Digital Twins
12-831: Introduction to AI for Predictive Analytics
18-660: Optimization
18-751: Applied Stochastic Processes
18-771: Linear Systems
18-685: Power Electronics for Electric Utility Systems
18-613 Foundations of Computer Systems
Controls
12-770: Autonomous Sustainable Buildings—Theory to Practice
12-774: Foundations of Intelligent Infrastructure Systems
12-831: Introduction to AI for Predictive Analytics
18-660: Optimization
18-751: Applied Stochastic Processes
18-771: Linear Systems
18-648: Embedded Real-Time Systems
18-813: Foundations of Reinforcement Learning
18-776: Non Linear Control
18-685: Power Electronics for Electric Utility Systems
18-675: Autonomous Control Systems