Electrical Engineering
|
Program code |
Intakes |
Duration |
Mode of study |
 Medium of instruction |
|
9520201 |
June December |
3-4 years |
Full-time |
English |
Introduction
The program’s learning outcomes seek to train PhD students with in-depth knowledge and scientific methods in Optimization in power system; New technology in power system; Renewable energy; Intelligent electric machine control; Robotics; Nonlinear control; New protocols for data networking; Semiconductor materials; and Data processing among others. In addition, PhD students are trained with independent research and life-long participation in the community to create new knowledge with scientific and practical significance in the above fields.
Curriculum apply for Ph.D. candidate with a Master's degree:
|
Course code |
Course title |
Credit |
Theory |
Practice Experiment Discussion |
|
A. Elective specialized courses |
12 |
12 |
0 |
|
|
EE801010 |
Applications of Heuristic Algorithms in Electrical Engineering |
3 |
3 |
0 |
|
EE801020 |
Advanced Topics in Optimal Power Generation among Power Plants |
3 |
3 |
0 |
|
EE801030 |
Advanced Topics in Management Tools in Electrical Distribution Systems |
3 |
3 |
0 |
|
EE801040 |
Solar Energy and Applications |
3 |
3 |
0 |
|
EE801050 |
Wind Energy and Applications |
3 |
3 |
0 |
|
EE801060 |
Advanced Flexible AC Transmission |
3 |
3 |
0 |
|
EE802010 |
Advanced RF System Design |
3 |
3 |
0 |
|
EE802020 |
Advanced Topics In Analog VLSI Design |
3 |
3 |
0 |
|
EE802030 |
Introduction to biomedical imaging systems |
3 |
3 |
0 |
|
EE802040 |
Modern Optical Communication Systems |
3 |
3 |
0 |
|
EE802050 |
Pattern Recognition and Machine Learning |
3 |
3 |
0 |
|
EE802060 |
Performance Modelling for Computer Communication Networks |
3 |
3 |
0 |
|
EE802070 |
Physical Electronics of Advanced Semiconductor Devices |
3 |
3 |
0 |
|
EE802080 |
Security of Communication Networks |
3 |
3 |
0 |
|
EE802090 |
Sparse Representation and Recovery |
3 |
3 |
0 |
|
EE802100 |
Stochastic Signal Processing |
3 |
3 |
0 |
|
EE803010 |
Microcomputer Control Systems of Electrical Drives |
3 |
3 |
0 |
|
EE803020 |
Cybernetics in Robotics |
3 |
3 |
0 |
|
EE803030 |
Mobile robots and applications |
3 |
3 |
0 |
|
EE803040 |
Advances measurements and applications in automatic control. |
3 |
3 |
0 |
|
EE803050 |
Power and Control Electronics |
3 |
3 |
0 |
|
EE803060 |
Modern Control Theory |
3 |
3 |
0 |
|
IT801010 |
Advanced topics in Artificial Intelligence |
3 |
3 |
0 |
|
IT801050 |
Advanced topics in Computer vision |
3 |
3 |
0 |
|
IN801100 |
Mathematical Modeling in Mechanics and Physics |
3 |
3 |
0 |
|
IN801110 |
Computational Artificial Intelligence |
3 |
3 |
0 |
|
IN801120 |
High Performance Computing |
3 |
3 |
0 |
|
B. Literature review |
4 |
4 |
0 |
|
|
EE801900 |
Research proposal |
4 |
4 |
0 |
|
C. Doctoral research topic |
6 |
6 |
0 |
|
|
EE801930 |
Research topic 1 |
3 |
3 |
0 |
|
EE801940 |
Research topic 2 |
3 |
3 |
0 |
|
D. Graduation |
|
70 |
70 |
0 |
|
EE801000 |
Doctoral Dissertation |
70 |
70 |
0 |
|
Total |
92 |
92 |
0 |
|
Note: 1 credit = 15 theory periods or exercises
= 30 periods of presentation, discussion or practice
Ph.D. candidate with a Bachelor's degree: not applicable
