When Machine Learning Meets Big Data in Wireless Communications

   School of Electronic Engineering and Computer Science

This project is no longer listed on FindAPhD.com and may not be available.

Click here to search FindAPhD.com for PhD studentship opportunities
  Dr Y Liu  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

School of Electronic Engineering and Computer Science (EECS) 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).

To apply, please follow the on-line instructions at the college web-site for research degree applicants (HTTP://www.qmul.ac.uk/postgraduate/research/subjects/). 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: http://www.eecs.qmul.ac.uk/phd/apply.php

Applicants seeking further information or feedback on their suitability are encouraged to contact Dr. Yuanwei Liu at [Email Address Removed] with 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 September 18th, 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).

Funding Notes

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 (www.eecs.qmul.ac.uk). The first supervisor is Dr. Yuanwei Liu (http://www.eecs.qmul.ac.uk/~yuanwei). In addition to the studentship, we also welcome applications from students supported by other funding with relevant background or experience.

Open days

PhD saved successfully
View saved PhDs