Cost & earnings at University of Virginia What students borrow here, and what they go on to earn
Computer Engineering graduates earn a median $94,408 Across 176 US programmes, two years after finishing
See the degree grade →The Master’s in Computer Engineering at the University of Virginia is a graduate programme that combines hardware and software training to prepare students for advanced roles in systems design, embedded computing and semiconductor technologies. It suits applicants with an undergraduate background in electrical engineering, computer engineering or computer science who want to pursue industry-facing engineering work or continue to doctoral study.
The programme provides a blend of theoretical foundations and hands-on engineering. Core areas include computer architecture, digital system design, embedded systems, VLSI and physical design concepts, real‑time and cyber‑physical systems, hardware/software co‑design, high‑performance computing and systems security. Coursework typically covers advanced topics such as processor microarchitecture, FPGA design and prototyping, low‑power design techniques, interconnects and networks-on-chip, and parallel systems.
Students follow a curriculum built from a combination of required graduate courses, electives and practical laboratory or project work. Program formats commonly include a thesis option for students pursuing research and publication, and a non‑thesis professional option that emphasises project‑based learning and an industry or capstone project. Electives are regularly available from closely related departments, allowing study in areas such as machine learning for systems, computer vision for embedded platforms, cybersecurity, and mixed‑signal circuits.
Applicants are normally expected to hold a bachelor’s degree in computer engineering, electrical engineering, computer science or a closely related discipline, with a strong record in mathematics, programming and core engineering courses. Typical application materials include official transcripts, a statement of purpose explaining academic and professional goals, academic or professional letters of recommendation, and a CV or résumé.
Evidence of relevant coursework or experience in digital design, data structures and systems programming strengthens an application. Applicants whose first language is not English must demonstrate English proficiency through an accepted test unless exempt. The programme evaluates applications holistically; some applicants may be admitted with conditional requirements or recommended preparatory coursework if gaps in background are identified.
Graduates move into roles designing and implementing computing systems across industry sectors. Common job titles include hardware engineer, FPGA/ASIC designer, embedded systems engineer, systems architect, firmware engineer and performance engineer. Alumni also work in the semiconductor and telecommunications industries, robotics and autonomous systems, defence and aerospace, IoT product development, and companies that build server and datacentre hardware.
The degree also prepares students for further research: many graduates continue to PhD programmes or take research positions in university labs and corporate R&D groups. The combination of hardware and systems skills is particularly valued by employers working on edge computing, acceleration for machine learning, and secure, low‑power device platforms.
The University of Virginia’s School of Engineering and Applied Science offers a collaborative environment with faculty who work at the intersection of hardware, systems and applications. Students benefit from access to modern electronics and systems laboratories, high‑performance computing resources, and opportunities to work on funded research projects alongside faculty.
UVA emphasises interdisciplinary training and strong industry links within the Mid‑Atlantic tech ecosystem, enabling internships, industry projects and networking with employers. Smaller cohort sizes and active faculty mentoring support hands‑on learning, while elective options across computer science and electrical engineering let students tailor their studies to particular technological challenges or career goals.
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