PhD Studentship - When Machine Learning Meets Big Data in Wireless Communications 2018, UK

Queen Mary University of London


September 18, 2018

Opportunity Cover Image - PhD Studentship - When Machine Learning Meets Big Data in Wireless Communications 2018, UK

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.

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).

All nationalities are eligible to apply for this studentship. We offer a 3-years fully funded PhD studentship, with a bursary ~£16.5K/year and a fee waiver (including non-EU students), supported by the School of Electronic Engineering and Computer Science of the Queen Mary University of London, UK. The first supervisor is Dr. Yuanwei Liu . In addition to the studentship, we also welcome applications from students supported by other funding with relevant background or experience.

To apply, please follow the on-line instructions at the college web-site for research degree applicants. At the page, select ‘Electronic Engineering in the list “FIND”’ and follow the instructions on the right-hand side of the web page.  Please note that instead of the ‘Research Proposal’ we request a ‘Statement of Research Interests’. Your statement (no more than 500 words) should answer two questions: 
(i)    Why are you interested in the topic described above? 
(ii)    What relevant experience do you have?
Please attach your CV, a transcript of records, and the title/s of your MSc dissertation/s.
In addition, we would also like you to send a sample of your written work, e.g., a chapter of your final year dissertation, or a published paper. More details can be found at:

Applicants seeking further information or feedback on their suitability are encouraged to contact Dr. Yuanwei Liuwith subject “Machine Learning & Wireless Communications PhD”. However, please, do not send documents as they will be reviewed only after the deadline. 

The closing date for the applications is 18th September 2018.
Interviews are expected to take place in the end of September/beginning of October 2018.
Starting date: November 2018- April 2019 (dates can be flexible).

For more information click "LINK TO ORIGINAL"  below.

Eligible Countries
Host Country
Study Levels
Publish Date
September 10, 2018
Link To Original