Funding & Eligibility
Fully Funded PhD Studentships (DfE Northern Ireland & CTA)
Queen's University Belfast is a Russell Group research institution. Doctoral researchers in the School of EEECS have access to competitive annual funding competitions:
Republic of Ireland & Common Travel Area (CTA)
Under the UK–Ireland Common Travel Area agreement, Republic of Ireland (ROI) citizens pay domestic UK Home tuition fees at Queen's University Belfast and do not require a student visa. This provides a direct, cost-effective route for Irish and eligible EU residents.
DfE Northern Ireland Postgraduate Studentships
The Department for the Economy (DfE) funds competitive annual PhD studentships covering 100% of approved tuition fees plus an annual tax-free living stipend (currently over £19,000/year). Open to eligible UK, ROI, and EU candidates meeting residency criteria.
Final-Year Students & Early Inquiries
You do not need to wait until your degree is finished to get in touch. Final-year undergraduate and Master's students expecting a First or 2:1 are encouraged to contact me during autumn/winter so we can prepare a strong proposal ahead of competition deadlines.
International & External Scholarships
International candidates can apply for university-level tuition fee awards. I also actively support strong candidates applying with external scholarships (e.g., home government sponsorships, national research councils, Marie Skłodowska-Curie networks, TÜBİTAK).
Doctoral Topics
Research Strands Open for Supervision
I welcome PhD proposals aligned with four core research strands. Interdisciplinary proposals combining machine learning, computer vision, and robotics are also welcome:
Robust Sensing for AI in Autonomy
Quantifying and mitigating perception degradation in adverse operating conditions (weather, sensor noise, occlusion, degradation) and domain shifts through resilient multi-modal sensor fusion and adaptive processing.
Run-Time Monitoring & Introspection
Investigating neural activation dynamics, epistemic uncertainty estimation, and real-time failure detection so that deep learning perception operates reliably and safely under out-of-distribution conditions.
Cooperative Autonomy
Developing multi-agent collaborative perception frameworks, spatial filtering, and resilient coordination mechanisms for fault tolerance in connected and automated vehicles (CAVs) and multi-agent systems.
Explainable AI (XAI)
Designing compact, explainable neural activation encodings and interpretable latent representations for real-time introspective safety monitoring, transparency, and accountability in autonomous and intelligent systems.
Mentorship
Supervision Approach & Prerequisites
What You Can Expect From Me
- Regular one-to-one supervision: Weekly or fortnightly meetings with dedicated focus on your research progress and career goals.
- Technical mentorship: Hands-on guidance in DNN introspection, neural activations, LiDAR 3D object detection, and AI safety.
- Compute and benchmark access: Access to high-performance GPU compute clusters and key benchmark datasets (nuScenes, KITTI, Waymo).
- Publication strategy: Active support in submitting to leading peer-reviewed venues (IEEE T-ITS, IEEE T-IV, CVPR, ICCV, ITSC).
- Timely, constructive feedback: Thorough reviews of paper drafts, dissertation chapters, and presentation slides.
What I Look For in Candidates
- Strong academic background: A First Class or Upper Second Class (2:1) Honours degree (or equivalent Master's) in Computer Science, Machine Learning, Robotics, or Electrical & Electronic Engineering.
- Programming proficiency: Strong Python skills and hands-on familiarity with deep learning frameworks (PyTorch strongly preferred).
- Curiosity and initiative: Willingness to read papers critically, question assumptions, and drive research questions forward.
- Communication skills: Clear written and spoken English, essential for research manuscripts and conference presentations.
- Safety-critical focus: Genuine motivation to work on trustworthy and robust AI for real-world automated systems.
Application Process
How to Inquire & Apply
To ensure a quick and productive review, please follow this three-step process:
Informal Email Inquiry
Send an email to h.yatbaz@qub.ac.uk with the subject line [PhD Inquiry] Your Name — Proposed Strand.
Please attach:
- Your current CV (including degree grades, GPA, and graduation date)
- Academic transcripts (undergraduate and postgraduate)
- A brief summary (1–2 paragraphs or 1 page) of your research interests and why you want to work on this topic
Discussion & Proposal Alignment
If there is strong research alignment, we will arrange an online video call to discuss your interests, refine a project outline, and determine the most suitable funding competition (DfE, university scholarships, or external awards).
Formal Application via QUB Direct
Submit your formal application through the Queen's University Belfast Direct Admissions Portal, naming Dr. Hakan Yekta Yatbaz as your proposed primary supervisor. The formal application requires reference letters, verified degree certificates, and the agreed research proposal.
Common Questions
Frequently Asked Questions
Are fully funded PhD studentships available for Northern Ireland, UK, and Irish students?
Can I apply while I am still finishing my undergraduate or Master's degree?
What research areas are currently open for PhD supervision?
What academic background do I need?
When should I get in touch?
Track Record
Student Supervision & Mentorship
Supervising postgraduate dissertation and undergraduate graduation projects at Queen's University Belfast across AI safety, computer vision, and autonomous systems:
Tin Yu
"Compact and Explainable Activation Encodings for Run-time Monitoring of 3D Object Detection under Domain Shift"
Joel Anderson
"Development of an AI Driven Web Application Assisting Students with Exam Preparation"
Yu-Zhi Wong
"Feature Lens: A Tool for Visualising and Comparing Neural Representations in Object Detection"
Jamal Salimov
"Vision Introspector: A GUI Tool for Analysing Object Detection in Driving Images"