Large-scale systems, whether chemical, fluid, electrical, or energy, require the orchestration of computational hardware, sophisticated algorithms, and advanced software. The Master of Science in Computational Systems Engineering (MS-CSE) is for students who want to work at the emerging interface of scientific computing, optimization, and complex physical or engineered systems.
Students will gain expertise in the computational frameworks necessary to drive innovation in application areas such as energy infrastructure, additive manufacturing, chemical process design, supply chain management, and electronic design automation.
The Department of Chemical Engineering offers the MS-CSE jointly with CMU's Department of Electrical and Computer Engineering, meaning that you can study directly with faculty in both departments. In 1.5 years of full-time study on Carnegie Mellon’s Pittsburgh campus, you can transform your computational and analytical skills into a powerful toolkit for leadership in a rapidly growing technical area.
Why earn a master's degree in Computational Systems Engineering at Carnegie Mellon?
Interdisciplinary edge
New technologies created at the interface of chemical engineering and electrical and computer engineering are in high demand. Students in the MS-CSE program gain a dual-domain perspective through a rigorous, systems-centric education.
The degree combines the strength of the Chemical Engineering and Electrical and Computer Engineering departments into a holistic view of large-scale systems. This combination allows you to understand the physical constraints of a system while applying the computational power necessary to optimize it. You’ll learn to develop mathematical models that describe physical systems, algorithms to solve them, and the software and hardware to implement them.
Industry-aligned training
CMU developed the MS-CSE degree in consultation with industry experts. The program focuses on real-world applications of systems engineering, such as VLSI chip design, programmable chemical reactions, multi-physics transport processes, and electrical grid management. Students are trained in cutting-edge techniques in large-scale modeling, systems engineering, optimization, applied mathematics, data science, and code development.
By mastering the computational hardware, algorithms, and software used to manage the complex operations in modern industry, you will develop a unique and highly-sought skillset. Graduates will enter the workforce ready to design next-generation solutions for the technical bottlenecks facing global industries.
Support for graduate students
Carnegie Mellon is proud of its culture of collaboration. Empowered by our strong community, our scholars and researchers are pushing the boundaries of what is possible. Master’s students are mentored by world-renowned faculty and propelled by a network of peers who share your drive for excellence.
As an MS-CSE student, you’ll be part of a cohort with access to resources, professional development, and community support from both the Chemical Engineering and Electrical and Computer Engineering Departments.
Curriculum for the Master's in Computational Systems Engineering
The MS-CSE curriculum is structured around three themes: Optimization, Applications, and Computation. The degree requires a minimum of 111 units in total over three full-time semesters. This includes 36 units of core coursework in Chemical Engineering, 36 units of core coursework in Electrical and Computer Engineering, 3 units of professional development coursework, and 36 units of elective coursework.
Students are required to take at least 24 units from each of the following themes, which include core coursework in both Chemical Engineering and Electrical and Computer Engineering. Examples of applicable courses include:
Optimization
- 06-642 Data Science and Machine Learning in Chemical Engineering (6 units)
- 06-606 Computational Methods for Large Scale Process Design & Analysis (12 units)
- 06-720 Advanced Process Systems Engineering (12 units)
- 18-660 Optimization (12 units)
- 18-661 Introduction to Machine Learning for Engineers (12 units)
- 18-667 Algorithms for Large-scale Distributed Machine Learning and Optimization (12 units)
Applications
- 06-635 Production and Supply Chain Optimization (12 units)
- 06-665 Process Systems Modeling (12 units)
- 06-663 Analysis and Modeling of Transport Phenomena (12 units)
- 06-722 Bioprocess design (12 units)
- 06-731 Molecular Machine Learning (12 units)
- 18-675 Autonomous Control Systems (12 units)
- 18-685: Power Electronics for Electric Utility Systems (12 units)
- 18-721 Advanced Analog Integrated Circuits Design (12 units)
- 18-771 Linear Systems (12 units)
Computation
- 06-611 Computer Science Tools for Engineers (9 units)
- 06-623 Mathematical Modeling of Chemical Engineering Processes (12 units)
- 06-643 Creating Scientific Research Software (6 units)
- 06-713 Mathematical Techniques in Chemical Engineering (12 units)
- 18-619 Introduction to Quantum Computing (12 units)
- 18-645 How to Write Fast Code I (12 units)
- 18-646 How to Write Fast Code II (12 units)
- 18-647 Computational Problem Solving for Engineers (12 units)
Electives
Students are required to take 24 units of graduate technical electives and 12 units of free electives, for a total of 36 units of elective coursework. A maximum of 12 units may be undertaken as independent project work under the supervision of a faculty member.
Professional development coursework
Students may take either 39-699 Career & Professional Development for Engineering Master’s Students (3 units) or 06-608 Graduate Professional Development Seminar (3 units).
Careers and outcomes for computational systems engineering students
The MS-CSE degree provides technical depth and professional versatility, whether you aspire to optimize global supply chains, design the next generation of power grids, or lead a data science team in the chemical industry. You’ll graduate ready to launch your career in sectors requiring sophisticated systems analysis.
See post-graduation salaries and destination information for recent CMU engineering master's graduates.
Take the next step
Define and solve technical problems involving large-scale systems with a Master of Science in Computational Systems Engineering from Carnegie Mellon University.