Artificial Intelligence in Unmanned Aerial Vehicle Networks
With the rapid development of control technology and manufacture business, unmanned aerial vehicles (UAVs), which were originally initiated by the military use, have gradually demonstrated the civil potentials to new applications and markets opportunities, such as emergency communication networks establishment, advanced cargo distribution, aerial photography and video streaming, and wildfire management, to name a few. Regarding communication areas, UAV-aided communication has been recognized as an emerging and promising technique in industry for its superior on flexibility and autonomy. For example, industry projects, such as Google Loon project, Internet-delivery drone in Facebook, and airborne LTE services in AT&T, have been deployed for providing airborne global massive connectivity. To assist 5G communications, promising research scenarios can be as follows: establishing temporal communication infrastructure during natural disasters, offloading traffic for dense networks, and data collection/processing for supporting Internet of Things (IoT) networks. This project aims to invoke the marriage of the AI and the communication for designing the deployment trajectory to establish flexible UAV communication 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 IoT techniques, such as UAV, V2X, wireless caching and mobile computing, 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, or similar.
• Good publications on AI or communication is a plus.
• Strong coding skills. (Python is required and C++ is a strong plus. Comfortable with the Linux environment.)
All nationalities are eligible to apply for this studentship. We offer a 3.5-years fully funded PhD studentship, with a bursary ~£16.5K/year (during the time in UK) 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), as well as monthly stipend of S$2,500 (during the time in Singapore) and the corresponding allowance (https://www.a-star.edu.sg/Scholarships/For-Graduate-Studies/A-STAR-Research-Attachment-Programme-ARAP), supported by A*STAR Research Institutes. The primary supervisor is Dr. Yuanwei Liu from QMUL (http://www.eecs.qmul.ac.uk/~yuanwei). The co-supervisor is Dr. Joey Zhou from A*STAR (https://joeyzhouty.github.io/).
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 “AI & UAV ARAP PhD”. However, please, do not send documents as they will be reviewed only after the deadline.
The closing date for the applications is March 30th, 2019.
Interviews are expected to take place in April.
Starting date: Sept. 2019 (dates can be flexible).