Applications are invited for a full PhD Scholarship starting January 2020 to undertake research on Ultra-reliable Low-latency Communications (URLLC). The successful applicant will be based in the School of Electronic Engineering and Computer Science (www.eecs.qmul.ac.uk), Queen Mary University of London, UK.
URLLC has been envisioned as one of three pillar use cases in 5G and beyond, which imposes stringent requirements for achieving extremely low latency and high reliability simultaneously. Some typical applications include vehicle-to-vehicle (V2V) communications, tactile internet, remote surgery, industrial automation, unmanned aerial vehicle (UAV) control communications, etc. However, the research on URLLC is still in its infancy due to its challenging requirements. To unlock URLLC, some promising research directions are identified as follows: 1) Resource management and/or channel coding under short packet transmission regime; 2) Cross-layer transmission design under stringent latency and reliability requirements (such as queue scheduling and access protocol design); 3) Machine learning based optimization methods to reduce the computational delay (such as deep neural networks, deep reinforcement learning), etc. The PhD will be based in the QMUL Communication Systems Research (CSR) Group (http://csr.eecs.qmul.ac.uk/
) with strong publication record and high international impact. The project will benefit from a recent purchase of several new GPU servers to support machine learning simulations.
All applicants should have a first-class honour degree or equivalent, or a MSc degree, in Electronic Engineering or Computer Science (or a related discipline). Applicants should have a good knowledge of English and ability to express themselves clearly in both written and spoken form. The successful candidate must be strongly motivated to undertake doctoral studies, must have demonstrated the ability to work independently and to perform critical analysis.
Applicants are expected to possess fundamental knowledge and skills in two or more of the following aspects:
• Excellent knowledge of wireless communications and/or signal processing.
• Prior experience in optimization theory such as convex optimization and control theory.
• Research background in applying machine learning for wireless communication.
• Publish international conference papers or IEEE journal papers.
• Good mathematical and programming skills.
All nationalities are eligible to apply for this studentship. We offer a 3-year fully funded PhD studentship supported by Queen Mary University of London including student fees and a tax-free stipend starting at £16,777 per annum. The first supervisor is Dr. Cunhua Pan (http://www.eecs.qmul.ac.uk/~cunhua/
). In addition to the studentship, we also welcome applications from self-funded students with relevant backgrounds.
To apply, please follow the online instructions specified by the college website for research degree: http://www.qmul.ac.uk/postgraduate/research/subjects/
. At this page, please click on ‘Electronic Engineering’ in the subject list and follow the instructions on the new 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?
In order to submit your online application you will need to visit the following webpage: https://www.qmul.ac.uk/postgraduate/research/subjects/electronic-engineering.html
. Please scroll down the page and click on “PhD Full-time Electronic Engineering - Semester 2 (January Start)”. The successful PhD candidate is expected to be a member of CSR group. You should mention this in your application.
Applicants seeking further information or feedback on their suitability are encouraged to contact Dr. Cunhua Pan at [email protected]
with subject “URLLC PhD Studentship”. All applications must be made via the website mentioned above.
The closing date for the applications is October 13th, 2019.
Interviews are expected to take place on October 16th, 2019.
Starting date: January 2020 (dates can be flexible).