PhD in Electrical and Computer Engineering
The Doctor of Philosophy in Electrical and Computer Engineering (PhD-ECE) degree program is a comprehensive program offering students a deep understanding of the foundations of research, enabling them to conduct their own dissertations in a variety of topics. Graduates of the program are well-prepared to address the complex challenges that arise in the fields of electrical and computer engineering.
Offered by the Departments of Electrical Engineering and Computer Science and Engineering, the curriculum includes training in advanced topics such as electronics, computer architecture, signal processing, telecommunications, smart grids, artificial intelligence and control systems.
Guided by world-class faculty at the forefront of their fields, students benefit from a dynamic research environment and collaborations with top academic, industry and government organizations to advance their expertise and pursue groundbreaking discoveries. The program is taught in English and is composed of three phases:
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core course, which includes a research methods course on key aspects of theoretical and applied research
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seven elective courses to suit the research interests of students with the aim of strengthening their knowledge in their area of research
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a doctoral dissertation, allowing students to develop cutting-edge research competencies and generate original scientific work that can be published in reputable international journals
Find our program brochure here.
The mission of the PhD-ECE program is to graduate a community of professionals equipped to lead innovative research and make significant contributions to the advancement of technology and knowledge in the field. Through rigorous academic training, interdisciplinary collaboration, and a commitment to ethical and socially responsible engineering practices, our graduates become experts in the field capable of pushing boundaries, addressing complex global challenges and inspiring the next generation of engineers and researchers.
Graduates of the PhD-ECE program will be prepared to:
- conduct independent, innovative and rigorous research in various areas of electrical and computer engineering
- contribute to the advancement of knowledge in the field through their proficiency in experimental design, data analysis, literature review and scholarly writing
- tackle complex engineering challenges that require expertise from multiple disciplines as a result of the interdisciplinary collaboration and research initiatives that fostered their creativity and innovation
- advance their careers in academia, industry or government as a result of the professional and leadership development opportunities presented during the program
Upon successful completion of this program, students will be able to:
- conduct independent, original research that advances the field of electrical and computer engineering, demonstrating expertise in solving complex engineering problems
- critically evaluate and synthesize existing research to develop innovative solutions for current and emerging challenges in electrical and computer engineering
- design and implement advanced experiments, simulations or models to generate new insights and validate theories in specialized areas of electrical and computer engineering
- effectively communicate and disseminate research outcomes in both written and oral formats
- demonstrate ethical and professional responsibility to ensure engineering solutions consider societal, environmental and global impacts.
The program admits both part-time and full-time students. Full-time students may be considered for a competitive full graduate assistantship for the duration of the program.
Applicants to the PhD-ECE degree program must:
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hold a master’s degree from an independently accredited university recognized by the UAE Ministry of Higher Education and Scientific Research and by AUS. Applicants with a master’s degree obtained outside the UAE must submit a Certificate of Equivalency or a Qualification Recognition report of their degree from the UAE Ministry of Higher Education and Scientific Research.
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have achieved a minimum master’s degree cumulative grade point average (CGPA) of 3.0 (on a scale of 4.00) or its equivalent.
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have attained a minimum iBT score of 80 or a minimum IELTS score of 6.5 (Academic Version) or a minimum EmSAT (Achieve English) score of 1550.
In addition to meeting the university's general requirements for admission to PhD degree programs, applicants to the PhD-ECE degree program are required to have completed a master of science degree in either electrical engineering, computer engineering or related areas from an independently accredited university recognized by the UAE Ministry of Higher Education and Scientific Research and by AUS.
For more information on admission requirements and the application process, please click here.
Apply now for our Doctor of Philosophy in Electrical and Computer Engineering program.
To qualify for graduation with a PhD-ECE degree, students must successfully complete a minimum of 54 credit hours with a minimum cumulative GPA of 3.00, as follows:
- a minimum of 24 credit hours (eight courses) of required and elective coursework:
- three credit hours in one required course
- a minimum of 21 credit hours (seven courses) of elective courses
- a zero-credit hour seminar
- a zero-credit qualifying examination
- a minimum of 30 credit hours of research work (dissertation)
The expected minimum duration for completion of the PhD-ECE degree program is four years. All graduation requirements must be completed within 10 years of admission to AUS as a doctoral student, inclusive of any leave, and graduation residence requirements must be met. For details, refer to the graduate catalog.
Required Courses
Students must successfully complete a minimum of 33 credit hours from the following required courses:
- ECE 703 Research Methods
- ECE 795 Doctoral Seminar
- ECE 790 Qualifying Examination
- ECE 799 Doctoral Dissertation (for a minimum of 30 credit hours)
Elective Courses
Students must successfully complete a minimum of seven courses (for a minimum of 21 credit hours) selected from the following list and subject to the approval of the Program Coordinator.
- ECE 735 Integrated Circuits for High-Speed Frequency and Clock Synthesis
- ECE 741 Experimental Methods in RF and Microwave Engineering
- ECE 743 Advanced Power Electronics
- ECE 745 Microwave Remote Sensing
- ECE 750 Reinforcement Learning
- ECE 760 Impairments Compensation in RF Transmitters
- ECE 761 Smart Grids and Renewable Energy
- ECE 762 Advanced Modelling and Optimization
- ECE 763 Advanced Queuing Systems
- ECE 764 Data-driven Science for Adaptive and Intelligent Control Systems
- ECE 765 Smart Multi-Object Design
- ECE 766 Smart City Architecture, Modeling and Applications
- ECE 767 Computational Intelligence
- ECE 768 Advanced Topics in Generative Artificial Intelligence Systems
- ECE 771 Wireless and Mobile Security
- ECE 772 Probabilistic Graphical Models
- ECE 773 VLSI Testing
- ECE 774 Advanced Wireless Communications
- ECE 794 Special topics in Electrical and Computer Engineering
With the approval of the Program Coordinator and the student’s advisor, a student may choose to take up to two elective courses outside of the list of elective courses.
Qualifying Exam
Students in the program must pass the qualifying examination (ECE 790). The exam will be on breadth of knowledge, understanding of fundamentals as well as topics related to the research area the student is working on as determined by the advisor(s) and the program coordinator. Full-time students should successfully complete the examination in the first two years. The following is a non-exhaustive list of possible research areas:
- Signal Processing, Communications and Networking
- Electromagnetics
- Computer Architecture and Systems
- Embedded and Cyber-Physical Systems and IoT (Internet of Things)
- Big Data Analytics
- Cybersecurity
- Robotics and Control Systems
- Power and Energy Systems
- Computer Vision and Image Processing
- Machine and Deep Learning and Artificial Intelligence
- Photonics and Optoelectronics
- Nanotechnology and Nanoelectronics
- Modeling and Testing
For more information, please see the graduate catalog.
PhD Dissertation (minimum of 30 credits)
Students must successfully prepare and defend a research dissertation that contributes to creating new knowledge in the field. For more information, please see the graduate catalog.
For more information on program requirements and course descriptions, please check our online catalogs.
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Faculty Name |
PhD Specialization |
Areas of Professional Expertise |
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Electrical Engineering (University of Kent, UK) |
Human emotions assessment, mental stress management, vigilance assessment and enhancement, brain source localization and assessment of spinal cord and brain injuries, flexible implantable electrodes and low power electronic devices |
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Electrical Engineering (University of Leeds, UK) |
Radio frequency integrated circuits and architectures, low power chip design, wireless sensors, implantable medical devices, energy harvesting techniques, radar systems, CMOS and MMIC technologies |
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Electrical Engineering (University of Minnesota, USA) |
Servo and adjustable-speed drive systems; power electronics; field-oriented control of ac motor drives; control of electro-mechanical systems using linear, nonlinear and adaptive control methods; real time digital control and DSP/Microcomputer-based controllers (hardware, software); systems of particular interest include rolling mill drives, robot manipulators mobile robots, and solar energy systems |
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Electrical Engineering (Carleton University, Canada) |
Wireless and digital communications, signals and systems |
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Mechanical Engineering ( University of British Columbia, Canada) |
Robotics and control systems |
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Electrical and Computer Engineering (University of Calgary, Canada) |
Wireless transceivers design, 5G RF circuits and systems, power amplifiers design, characterization and linearization, digital predistortion techniques, satellite transmitters, impairments compensation for wireless and satellite systems |
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Electrical and Computer Engineering (Imperial College London, UK) |
Control systems, robotics, power electronics, renewable energy, electric vehicles, artificial intelligence |
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Electrical and Computer Engineering (University of Arizona, USA) |
Wireless communications, multimedia communications, cognitive radios, optimal resource allocation in next generation systems and performance evaluation, and QoS provisioning over wired and wireless networks |
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Electrical Engineering (University of Mississippi, USA) |
Performance evaluation of wireless communication systems, cognitive radios, channel modeling, resource allocation in next generation systems, spectrum coexistence, 5G and beyond cellular systems |
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Electrical Engineering (University of Washington, USA) |
Radar systems, biomedical signal processing |
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Electrical and Computer Engineering (University of Calgary, Canada) |
Power systems, power system protection, energy management, electric vehicles, smart grids |
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Electrical Engineering (Colorado State University, USA) |
Microwave non-destructive testing and imaging, energy harvesting, ultrasound and microwave biological applications, materials characterization |
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Electrical Engineering (Ohio State University, USA) |
Electric drives, EV, renewable energy systems, power electronics |
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Electrical and Computer Engineering (University of Waterloo, Canada) |
Smart grid, renewable DG, distribution system planning, microgrids, electric transportation, storage systems, demand side management and bulk power system reliability |
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Electrical and Computer Engineering (University of Illinois at Urbana-Champaign, USA) |
Computer vision, image processing, machine learning, object recognition, object detection, face recognition, soft biometrics, sparse coding and deep learning |
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Electronics Engineering (University of South Australia, Australia) |
MEMS, carbon nanotubes-based sensors |
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Electrical Engineering (University of Manitoba, Canada) |
Microwave biomedical imaging, computational electromagnetics, optimization algorithms, millimeter wave channel characterization, non-destructive testing and evaluation |
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Computer Engineering (Western University, Canada) |
Cloud computing, mobile cloud computing, fog computing, decentralized scheduling, content-based networks, economic-based optimization models and approaches, privacy in distributed systems, smart spaces |
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Computer Engineering (Purdue University, USA) |
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Electrical Engineering (Vanderbilt University, USA) |
Embedded systems, industrial internet of things, smart grid, smart manufacturing and industry 4.0 applications in smart cities and PLCs applications |
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Electrical Engineering and Computer Science (Massachusetts Institute of Technology, USA) |
Machine learning, information theory, wireless communication |
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Computer Science and Engineering (University of Michigan-Ann Arbor) |
Cyber security, mobile applications, smart cities and design optimization |
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Computer Science (University of Southampton, UK) |
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Electrical and Computer Engineering (Virginia Commonwealth University) |
Medical imaging and informatics, machine learning and data mining |
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Software Engineering, (University of Ottawa, Canada) |
Testing of systems modeled as timed, untimed and extended finite state machines; synthesis of distributed systems (decomposition of Petri net services, solving automata equations); optimization with application to protocol synthesis, testing and cloud computing |
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Electrical Engineering (University of Texas at Dallas, USA) |
Optical and wireless networks, wavelet applications to optical switching and computing, multimedia networking |
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Computer Science/ Artificial Intelligence (National Polytechnic Institute of Grenoble, France) |
Cognitive computing and systems, adaptation and learning, knowledge discovery, nature-inspired computing, approximate reasoning, decision support, intelligent robotics, autonomous vehicles, sensor fusion, medical informatics and education |
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Electrical and Computer Engineering (University of Arizona, USA) |
Power consumption in VLSI circuits, hardware description languages, FPGAs applications, innovative uses of emerging technology |
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Electronic Systems Engineering (University of Essex, UK) |
Digital video coding and transcoding and machine learning |
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Computer Science (University of Minnesota, USA) |
Internet of things, deep learning, big data, educational analytics |
For more information about the program, please contact:
Dr. Ahmed Osman
Professor and Head, Department of Electrical Engineering
+971 6 515 2556, [email protected]
Shena Rosa
Administrative Assistant
+971 6 515 2474, [email protected]
