PhD Studentship in When Machine Learning Meets Big Data in Wireless Communications
Recent several decades have witnessed the exponential growth in commercial data services, which lead to step in the so-called big data era. The pervasive increasing data traffic present both the imminent challenges and new opportunities to all aspects of wireless system design, such as efficient wireless caching, drone base station deployment and adaptive nonorthogonal multiple access design. Machine learning, as one of the most promising artificial intelligence tools, has been invoked in many areas both in the academia and industry. Nevertheless, the application of machine learning in wireless communication scenarios is still in its infancy, which motivates to develop this phD project. The aim of this phD project is to use social media data to predict the requirements of mobile users for improving the performance of wireless networks.
Qualifications:
All applicants should hold a masters level degree at first /distinction level in Computer Science or Electronic Engineering (or a related discipline). Applicants should have a good knowledge of English and ability to express themselves clearly in both speech and writing. The successful candidate must be strongly motivated for doctoral studies, must have demonstrated the ability to work independently and to perform critical analysis.
Candidates are asked to possess fundamental knowledge and skills in two or more of the following areas:
- Excellent background in communication theory and signal processing algorithms. Good knowledge of emerging 5G and IoT techniques, such as NOMA, wireless caching and mobile computing, UAV, V2X, etc.
- Prior experience/education in both theory and practice of machine learning.
- Hands on experience using one of the following deep learning libraries: Tensorflow, PyTorch, Theano or similar.
- Good coding skills. (Python and C++ are considered a plus).