The Computer Engineering master's program trains students to design hardware, software, and networking systems for the computers of today and tomorrow.
Jointly administered by the Department of Electrical and Computer Engineering and Department of Computer Science, this master's program incorporates research and teaching across disciplines to provide students with a well-rounded approach to the dynamic field of computer engineering.
Students personalize their path of study and regularly access professors both in small classes and out of class. At least four lecture-based courses must be taken from a published list of computer engineering core courses. From these core courses, at least one course must be taken from each of the following three core areas:
Master's degrees require a minimum of 30 credits hours and the fulfillment of at least 10 courses at the 100-level or above with grades of S (satisfactory) or at least a B-.
The School of Engineering's Graduate Cooperative Education (Co-Op) Program provides students with the opportunity to apply the theoretical principles they have learned in their coursework to real-world engineering projects. Gain up to six months of full-time work experience, build your resume, and develop a competitive advantage for post-graduation employment. Learn more about the Co-Op Program.
Computer hardware is now being designed with specialized computer cores for specific applications. Computing devices are connected to complex computer networks and the internet, whether they are large servers in a cloud, or embedded Internet-of-Things devices. With software and hardware systems gaining in complexity, computers and designers must go beyond functional correctness and be concerned with power consumption, security, and reliability.
The program gives students a unique perspective on how electrical and computer technology can be used to solve important human problems. With expert faculty, cutting-edge research, and innovative facilities, our students are given the opportunity and resources to make significant contributions to the field and become leaders in industry, government, and academia.
We recognize that attending graduate school involves a significant financial investment. Our team is here to answer your questions about tuition rates and scholarship opportunities.
Please contact us at gradadmissions@tufts.edu.
Average Salary: $138K+
Projected Job Growth (2022-2032): 5%
*Sources: Average salary and projected job growth statistics are from the U.S. Bureau of Labor Statistics Occupational Outlook Handbook.
Research/Areas of Interest: Machine Learning; Systems Biology; Metabolic Engineering, computer-aided design for integrated circuits
Research/Areas of Interest: computer architecture, computer systems, power-aware computing, embedded systems, mobile computing, computer systems for machine learning, workload characterization, quantum computing, learning sciences and computer systems for human subjects research
Research/Areas of Interest: Computer architecture, parallel processing, computer networking, hardware description languages, simulation and programmable logic design, engineering education.
Research/Areas of Interest: data science, software systems engineering, performance analysis, system, network, and data management
Research/Areas of Interest: computational molecular biology, data science, graph algorithms, network science, discrete mathematics
Research/Areas of Interest: emerging technologies, non-volatile memories, SoC design, hardware for machine learning, noise modeling and reliability
Research/Areas of Interest: design of silicon-based mixed-mode VLSI systems (analog, digital, RF, optical), analog signal processing, and optoelectronic system-on-chip modeling and integration for applications in optical wireless communication and biomedical imaging
Research/Areas of Interest: nanophotonics, optical beam shaping, neuroengineering, chip-scale imaging and microscopy, quantum information systems Research Website: https://sites.tufts.edu/amohanty/
Research/Areas of Interest: Signal processing; image processing; simulation modeling