PhD Studentship – School of Electronics, Electrical Engineering and Computer Science (EEECS)

at Queen's University Belfast - School of Electronics, Electrical Engineering and Computer Science (EEECS)
Published March 7, 2023
Location Belfast, United Kingdom
Category Machine Learning  
Job Type Scholarship  


The School of Electronics, Electrical Engineering and Computer Science (EEECS) at Queen’s University Belfast is currently offering a number of funded PhD opportunities – up to 26 scholarships- commencing in October 2023.  EEECS aims to enhance the way we use technology in communication, data science, computing systems, cyber security, power electronics, intelligent control, and many related areas.

As a PhD student you’ll be part of a dynamic doctoral research environment and will study alongside students from countries worldwide; we supervise students undertaking research in key areas including: computing systems, artificial intelligence, cybersecurity, power electronics, robotics, wireless communications, cybersecurity and sensor-based systems. You’ll have the opportunity to develop your research potential in a vibrant research community that prioritises the cross-fertilisation of ideas and innovation in the advancement of knowledge.

You will have the opportunity to develop and refine appropriate research skills and learn how to carry out significant, high-quality, original research in your specified research area, as well as having the opportunity to work with some of the most passionate internationally recognised researchers in their field.  Our PhD programmes also provide our students with the opportunity to acquire extensive training in research techniques. You will also have free access to a Structure Training plan comprised of more than 30 training courses covering from technical skills – programming, ML/AI, hardware - to leadership and time management.

Scholarships are primarily address to local and UK resident students. They provide fully paid PhD fees plus and annual tax-free stipend (~£19000) for the 3 year duration of the PhD project. This is equivalent to an entry level job in industry of > £25000. In addition, EEECS will provide Travel awards to cover the cost of trips to conferences and summer schools. Finally, EEECS PhD student will be given the opportunity to experience teaching demonstration to get to know what is to be an academic (maximum ~£500 extra per month) and apply to the School thesis and paper awards (each fitted with economic incentive).

For more information on the School’s PhD Opportunities and how to apply, please visit:

Computer Science:

Electrical and Electronic Engineering:

Deadline for Applications: Wednesday 15 March 2023

An investigation of machine learning systems security with a forensics perspective

Cyber Attack Prediction for Proactive Cyber Defence

Dynamic Data-Oriented Serverless Fog Computing

Empirical Elasticity between the Fog and Cloud

Explainable AI for Hardware Security

Hardware Security for Approximate Computing

Holographic MIMO Communications

Identity management of digital knowledge assets in metaverse

Information security establishment using computer architectural solution

Integrated side-channel analysis in Physical Uncloneable Function

Knowledge Modelling for Education

On a Holistic Design-oriented Methodology for Microservice Boundary Identification

Quantifying uncertainty for risk assessment in cyber-physical systems

Remote healthcare security

Responsibility Debt and SE Practice (ResponSE)

Secure searching over medical data in a multi-client setting

Side-channel analysis and countermeasure on Lightweight Post Quantum Cryptographic

Software Architectural Tactics and Patterns for Privacy Preservation in Internet of Things

Software-based approaches for IoT attack mitigation

Smart Abuse? Technology-facilitated abuse ("tech abuse") through the Internet of Things (IoT) in the context of Intimate Partner Violence (IPV)

Topical Information Search based on Deep Learning

Trustworthiness of Machine Learning through Brain-inspired Architectures

UI Security Attack Detection in Malware Design at Scale

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