Queen Mary University of London invites applications for a full PhD Scholarship starting in January 2022 (or as soon as possible thereafter) to undertake research in the area of Resource Management for Edge/Serverless Computing.
Recent technological developments and paradigms such as Serverless computing, Internet of Things (IoT), and processing at the network edge, bring new opportunities for Cloud computing. However, they also pose several new challenges and create the need for new approaches and research strategies, as well to revisit the models that were developed to address issues such as scalability, elasticity, reliability, latency, sustainability. Emerging technologies such as Edge and Serverless Computing and the IoT present new challenges which cannot be easily met by current resource provisioning and scheduling techniques, frameworks and mechanisms. Our future research aims to develop systems using the latest Artificial Intelligence (AI) and Machine Learning (ML) techniques which will be capable of supporting these technologies to meet the requirements of modern IoT applications.
This studentship will explore the intersection of Edge/Serverless and ML/AI for modern IoT applications. The scope of the project is quite broad. Applicants are encouraged to suggest their own interest and refine the research direction accordingly.
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 £17,609 per annum. 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 degrees: http://www.eecs.qmul.ac.uk/phd/how-to-apply/. Steps 2 onwards are applicable in this case. Please note that 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 addition to this, we would also like you to submit a sample of your written work. This might be a chapter of your final year or masters dissertation, or a published conference or journal paper.
In order to submit your online application you will need to visit the following webpage: https://www.qmul.ac.uk/postgraduate/research/subjects/computer-science.html. Please scroll down the page and click on “PhD Full-time Computer Science - Semester 2 (January Start)”. The successful PhD candidate will be a member of the Networks Research group. You should mention this in your application.
Applicants interested in the post, seeking further information or feedback on their suitability are encouraged to contact Dr. Sukhpal Singh Gill at [email protected] with the subject “Resource Management for Edge/Serverless Computing”. All applications must be made via the website mentioned above.
The closing date for applications is 15 September 2021.
All applicants should have a first-class honor degree or equivalent, or an MSc degree, in Computer Science or Electronic Engineering (or a related discipline). Applicants should have a good knowledge of English and the ability to express themselves clearly in both written and spoken form. The successful candidate must be strongly motivated to undertake doctoral studies, as well as must-have demonstrated the ability to work independently and perform critical analysis. A record of publishing research in international conferences and/or journals is highly desirable, as well as a strong track record of working in international teams.
The essential selection criteria will include:
Experience in Cloud, Edge, Serverless Computing.
Good coding skills in Python, Matlab and/or Java.
Good knowledge of data science methods.
Understanding of Machine Learning and IoT.
Ability to work independently or as part of a team.
The desirable selection criteria will include:
Experience and knowledge of machine learning techniques.