Why Pursue a Computer Engineering Master's Degree
The MS in Computer Engineering stresses both theoretical and practical aspects of electrical and computer engineering by combining the teaching and research expertise of the university community with the resources of other San Antonio research institutions. Graduate faculty are very active in conducting cutting-edge research projects that provide both research experience and financial support for graduate students.
Thesis Track
A thesis option is offered for students who want the opportunity to obtain expertise in research and who may be interested in pursuing the doctoral degree in computer engineering or electrical engineering.
Non-Thesis Track
A non-thesis option is offered for students who want a practical industrial applications-oriented degree.
College of AI, Cyber and Computing
This program is part of the UT San Antonio College of AI, Cyber and Computing, which advances expertise in artificial intelligence, cybersecurity, computing and data science to meet the needs of a rapidly evolving technology landscape. The college fosters interdisciplinary collaboration, applied research and engagement with industry partners to drive innovation and address real-world challenges.
Admission & Application Requirements
Applications are submitted through the UT San Antonio Graduate Application. Please upload all required documents (listed below) on your UT San Antonio Graduate Application. It is the applicant’s responsibility to ensure completion and submission of the application, a nonrefundable application fee, and all required supporting documents are on file with UT San Antonio by the appropriate application deadline.
| Computer Engineering (MS) | ||
|---|---|---|
| Required Degree | Bachelor's Degree from an accredited college or university in the United States or have proof of equivalent training at a foreign institution. | |
| Minimum GPA | 3.0 (on a 4.0 scale) Departments may consider GPA of last 60 semester credit hours | |
| Coursework | Completed adequate credit hours or foreign institution equivalent coursework as preparation for the program. | |
| Transcripts* | Required from all institutions attended; international transcripts must be recorded/translated to English | |
| Credential Evaluation | Required if you have earned university-level credit from foreign institutions. Submit an evaluation of your transcripts from FCSA or any NACES-approved credential evaluation agency. | |
| English Language Proficiency | 79 TOEFL iBT / 6.5 IELTS / Duolingo 100 For exams taken on or after January 21, 2026: We require a minimum TOEFL iBT score of 4.0. |
|
| *Unofficial transcripts will be taken into consideration for admissions; however, if admitted into the program, you must submit official transcripts to the University. | ||
Application Deadlines
Applicants are encouraged to have their admission file completed as early as possible. All applications, required documents and letters of recommendation, if applicable, must be submitted by 5:00 PM U.S. Central Time on the day of the deadline. Deadlines are subject to change.
| Computer Engineering (MS) | |||
|---|---|---|---|
| Timing on Admission Decision: Completed applications will be reviewed as they are received. Decisions generally will be made and sent to applicants within 4 to 6 weeks of receiving the completed application. | |||
| Application Deadlines for: | Priority | International | Domestic |
| Spring 2027 | Not Available | October 1 | October 1 |
| Summer 2027 | Not Available | March 1 | March 1 |
| Fall 2027 | February 1 | June 1 | August 1 |
| Spring 2028 | Not Available | October 1 | October 1 |
| Summer 2028 | Not Available | March 1 | March 1 |
Funding Opportunities
Learn MoreCareer Options
UT San Antonio prepares you for future careers that are in demand. The possible careers below are based on data pulled by a third-party tool called Emsi, which pulls information from sources like the U.S. Bureau of Labor Statistics, U.S. Census Bureau, online job postings, other government databases and more to give you regional and national career outlook related to this academic program.
Graduate courses are offered mostly in afternoon and evening. About half of courses are taught in the afternoon (2:00PM-5:00PM) and another half in the evening (5:00PM- 8:15PM).
Frequently Asked Questions
The Master of Science (MS) in Computer Engineering at UT San Antonio is a graduate STEM program designed to prepare students for advanced technical and leadership roles in industry, government, or academia.
It is ideal for people who want a rigorous, engineering‑centric graduate degree that spans hardware, software and systems, with optional research depth.
The Master of Science (MS) in Computer Engineering at UT San Antonio covers a broad, integrated curriculum spanning hardware, software and systems, with strong ties to AI, cybersecurity and advanced computing. Some of the topics include:
- Computer architecture
- Advanced digital systems
- Microprocessor- and microcontroller-based design
- VLSI design methodologies
- Low‑power and high‑performance circuit design
- Machine learning systems
- AI-enabled embedded platforms
- Hardware support for AI workloads
This program typically takes 2 years as a full-time student to complete.
Graduates of the MS in Computer Engineering at UT San Antonio are prepared for a wide range of technical, research and leadership roles across industry, government and academia. Because the program emphasizes hardware–software systems, embedded computing, AI-enabled platforms and security, alumni tend to be competitive for roles that go beyond entry‑level positions.
While not guaranteed, there are multiple forms of financial support available on a competitive basis including graduate research assistantships, and scholarships and fellowships at the department, college and university levels.
In the News
NVIDIA, a global technology company well-known for their development of advanced computer chips and other systems that power AI, partnered with the College of AI, Cyber and Computing to host a full-day workshop for students at the Valero Engagement Center in San Pedro II. The workshop gave students a hands-on introduction to agentic AI, a form of AI system designed to pursue goals using its own planning processes and available resources with limited human direction. The workshop provided around 100 students the foundation needed to further study these systems and even develop them to be safe and secure tools.
Read the full story “NVIDIA workshop gives students hands-on agentic AI training” to learn more about this unique hands-on learning opportunity.
Researchers at the MATRIX AI Consortium may have solved a significant roadblock in artificial intelligence learning with the development of a new accelerator chip: Genesis. Dhireesha Kudithipudi, PhD, a UT San Antonio professor of computer engineering, worked with her team to create the chip in answer to a phenomenon known as “catastrophic forgetting,” in which artificial intelligence lacks the ability to retain previous knowledge after learning knew information, causing it to forget what it had previously learned. Genesis was designed to mimic the human brain’s ability to retain information and learn new information at the same time throughout its lifetime. The chip is also designed to be energy efficient and usable in areas with no data center or little to no connection, allowing it to be used in technology like implanted medical devices and field-deployed drones, going years without needing to recharge or reset. This could mark a significant advancement in the use of artificial intelligence.
Read the full story “Meet Genesis: the AI chip that doesn’t forget” to learn more about this innovative project.
For more news stories, visit UT San Antonio today.
