United Arab Emirates University

What you'll study

This programme combines computer engineering foundations with specialised AI topics. Early years emphasise mathematics, physics and programming fundamentals; later years move into digital systems, computer architecture and software engineering alongside machine learning and AI subjects.

• Core engineering and computing: calculus, linear algebra, probability and statistics, circuit theory, digital logic, signals and systems, computer organisation and operating systems.
• Programming and software: introductory and advanced programming (typically C/C++, Python or equivalent), data structures and algorithms, databases, software engineering and embedded programming.
• Artificial intelligence and data science: machine learning, deep learning, data mining, computer vision, natural language processing, reinforcement learning and AI project work.
• Systems and integration: microcontrollers and embedded systems, robotics fundamentals, real-time systems, sensor interfacing and IoT applications.
• Laboratory and practical modules: extensive lab work, programming laboratories, hardware prototyping and AI experiment labs that reinforce theory with hands-on experience.
• Professional and ethical topics: engineering design, project management, AI ethics and legal considerations, and communication skills for engineers.
• Final project/capstone: a substantial team or individual design project integrating computer engineering and AI to solve a realistic problem, often with industry or research supervision.

The curriculum is structured across multiple academic years with progressive specialisation and includes opportunities for summer internships, elective choices in advanced AI or systems topics, and research projects with faculty.

Entry requirements

Entry is typically based on a recognised secondary school certificate with strong performance in mathematics and physics. Applicants should have completed advanced-level mathematics (or equivalent) and show aptitude for analytical and technical study.

• Recognised high school diploma or equivalent with competitive grades in mathematics and science subjects.
• English language proficiency demonstrated by prior education in English or an accepted test score (the university may accept a variety of English evidence).
• Some applicants may be required to complete a foundation year or preparatory programme if their prior qualifications do not include required mathematics or physics content.
• Selection may consider entrance tests, interview outcomes or placement exams depending on faculty admission policies.

Career prospects

Graduates enter roles that bridge hardware, software and intelligent systems. The degree prepares students for both industry positions and further study.

• Software engineer, AI engineer or machine learning engineer in technology companies and startups.
• Embedded systems or firmware developer for electronics, automotive and IoT industries.
• Robotics engineer, computer vision specialist or natural language processing developer in research and product teams.
• Data scientist or analytics specialist applying statistical and machine learning methods to business and engineering problems.
• Opportunities in government, defence, energy, healthcare and finance where intelligent systems and automation are applied.
• Progression to postgraduate study (MSc or PhD) in computer engineering, artificial intelligence, robotics or related research areas.

Why study at United Arab Emirates University

United Arab Emirates University is the UAE's flagship national university with a comprehensive engineering faculty and a track record of regional research and industry engagement. The campus offers modern teaching and laboratory facilities where students can work on hardware prototyping, software development and AI experiments under academic supervision.

• Access to multidisciplinary faculty with strengths across computer engineering, AI and applied research.
• Strong links with local and regional industries and public-sector organisations that provide internship and project opportunities.
• Practical learning emphasis through labs, capstone projects and experiential placements to build employable skills.
• Pathways to postgraduate research or professional specialisation in a rapidly growing regional technology sector.