About the Project
Energy-Data Dynamic Multiobjective Optimisation for Cloud-Edge Systems
How can we make future digital infrastructure smarter, faster, and more sustainable? This project explores dynamic multiobjective optimisation for Cloud-Edge computing, addressing the growing need to manage energy, data, and computing resources efficiently.
Cloud-Edge systems support emerging technologies such as the Internet of Things (IoT), smart cities, healthcare, autonomous systems and industrial automation. As data volumes and computing demands increase, there is a need for intelligent approaches that can reduce energy consumption while maintaining performance and Quality of Service (QoS).
The project aims to develop an optimisation framework that dynamically manages computing and communication resources across Cloud and Edge environments. Key objectives include reducing energy consumption and latency, improving resource utilisation, and maintaining QoS under changing workloads, network conditions, and resource availability. The student will investigate advanced optimisation techniques, including Pareto-based optimisation, evolutionary algorithms, swarm intelligence, and adaptive optimisation. Machine-learning approaches may also be explored to predict workload and energy requirements, enabling more proactive and intelligent resource-management decisions.
A Cloud-Edge environment will be modelled and simulated using workload, computing, network, data-transfer, and energy information. Proposed solutions will be evaluated against baseline approaches using measures such as energy efficiency, response time, resource utilisation, scalability, and QoS. The project will provide practical experience in designing and evaluating intelligent optimisation solutions.
Expected outcomes include optimisation algorithms, simulation models, experimental results, and recommendations for developing greener, faster, and more adaptive Cloud-Edge infrastructures. The project has potential to contribute to sustainable computing and the intelligent management of future digital services.
The student will receive regular supervision, technical guidance, and practical training throughout the project. The student will also develop valuable research skills in research methods, literature review, experimental design, scientific writing, and presentation.
This project is ideal for students interested in AI, cloud computing, data science, optimisation, and sustainable technology, offering an opportunity to work on a timely research challenge with strong academic and real-world relevance.
This project is expected to start in September 2027.
Before you apply: We strongly recommend that you contact the supervisors for this project before you apply.
How to apply:
To be considered for this project you must complete a formal application through our online application portal. If you already have an applicant account this link will directly open an application for PhD School of Engineering Scholarships. If you don’t already have an applicant account, please follow the instructions here.
When applying, please specify the full title and supervisor/s of the project, details of your previous study, and names and contact details of two referees. You must also upload a Supporting Statement describing the motivation to apply to the project, your CV and transcripts of awarded and in-progress university qualifications. Please note late or incomplete applications will not be considered.
Equality, diversity and inclusion are fundamental to the success of The University of Manchester and central to all our activities. A diverse research community strengthens creativity, productivity and quality, while increasing the societal and economic impact of our work. We welcome applicants from all career paths, backgrounds and sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation or transgender status.
We welcome applications from candidates returning to study after a career break or experience in other roles. Flexible study arrangements may be available, including part-time study at 50%, 60% or 80%, subject to the requirements of the project and funder.
Eligibility: The standard academic entry requirement for this PhD is an upper second-class (2:1) honours degree in a discipline directly relevant to the PhD Computer Science or related discipline (or international equivalent) OR any upper-second class (2:1) honours degree and a Master’s degree at merit in a discipline directly relevant to the PhD Computer Science or related discipline (or international equivalent).
Knowledge of cloud/edge is desirable.
This project will remain open until filled.
If your application is submitted by 1st November 2026, you can expect a decision by 18th December 2026.
If your application is submitted by 15th January 2027, you can expect a decision by 30th March 2027.
Self or externally funded students can also be considered for this project.
FSE_SoE
Funding Notes
Funding for this project covers tuition fees, UKRI minimum annual stipend (currently £21,805/annum) and up to a £2.5k/annum research training support grant for the full duration of the 3.5-year programme.