A number of Postgraduate Research Studentships are available within the School of Electronics Electrical Engineering and Computer Science for home and EU students enrolling in 2019.
Further information on potential projects which may be successful for an award can be obtained on our website
Please note that not all projects are subject to internal QUB funding.
If you have a project of your own, then you should contact a possible supervisor about its viability.
A minimum 2.1 honours degree or equivalent in Electrical and Electronic Engineering, Computer Science or relevant degree is required.
More information on funding of a PhD can be found at https://www.qub.ac.uk/sites/graduateschool/PostgraduateFunding/
There are a number of PhD studentships available for applicants in the UK and EU countries who wish to undertake full-time research at Queen's. These 3 year and 4 year PhD studentships, potentially funded by the Department for Employment and Learning (DEL) or Engineering and Physical Sciences Research Council commence on 1 October 2019.
In some instances, conditions on awards with eligibility for both fees and maintenance (£15,038 in 2019/20) depends on the applicants being either an ordinary UK resident or those EU residents who have lived permanently in the UK for the 3 years immediately preceding the start of the studentship. Non UK residents who hold EU residency may also apply but if successful may receive fees only.
Applicants should apply electronically through the Queen’s online application portal at: https://dap.qub.ac.uk/portal/
Applications received after 28th February 2019 may not be processed in time to receive a DfE award.
Applicants will be required to undertake an interview.
Dr. Georgios Karakonstantis
- Energy-Quality Scalable Processors based on Data Mining
- A Smart Data Analysis System for Food Safety based on Pattern Recognition
- Failure Prediction based on Data Mining for Energy-Efficient & Dependable Cloud/Edge Ecosystems
- In-Time and In-Space Sharing of FPGA Resources in Cloud/Edge Ecosystems
- Intelligent Memory Access Pattern Scheduling for Power and Performance Optimization
Dr. J. McAllister
- Biophysical EMG Analysis for Intelligent System Control
- FPGA-based Graph Signal Analysis
- Machine Learning in Intelligent Autonomous Devices
- Quantum Acceleration for High Performance Data Processing
- Quantum Error Correction using Field Programmable Gate Array
(EPSRC Doctoral Training Partnership with Keysight Technologies)