Research Fellow

About the job:

Join the Centre for Wireless Innovation as a Research Fellow to lead the AI and data analysis initiatives for the CP-MATS (Channel Prediction for Multi-Access Technology Selection) project funded through the EPSRC TITAN Hub. This role focuses on developing a proactive, AI-driven approach for access technology selection in next-generation 6G wireless systems.

You will lead the design of a novel hybrid AI model, applying advanced machine learning and modelling for complex systems to predict channel quality dynamically. You will collaborate closely with Tyndall Institute, Ireland and the University of Cambridge, UK as well as exploring the use of the JOINER infrastructure to help collect and synthesize data required to train and validate your AI models.

This position is ideal for a researcher with a strong background in data-driven optimisation and AI, ready to translate foundational research into real-time, wireless capabilities.

Please note this post will require the successful candidate to have immediate availability to start asap.

About the person:

As a Postdoctoral Research Assistant on the CP-MATS project, your core responsibility will be driving the development of a novel hybrid AI model for dynamic channel prediction. You will demonstrate how AI-driven channel prediction can assist with Multi-Access Technology Selection (MATS) and collaborate closely with our strategic partners in Ireland to integrate complex sensing data into your models.

To be successful in this role, you must meet the following essential criteria:

• Hold a PhD in Electronics, Electronic Engineering, Computer Science, Physics, or a closely related discipline.
• Have research experience in techniques including multiobjective optimisation, co-evolutionary algorithms.
• Have extensive AI and machine learning capabilities including extreme learning machines, handling imbalanced data, deep learning and neural networks.
• Demonstrate an established track record of publishing high-quality research in leading peer-reviewed journals.
• Evidence of strong publication record commensurate with stage of career.

Additionally, we are highly interested in candidates who bring specific expertise in dynamic multi-objective optimisation and online spatiotemporal modelling. While not strictly essential, prior experience applying machine learning to wireless signals, such as activity recognition and device-free localisation using WiFi, 5G etc. will be considered a significant advantage.

This is a fixed term post for 6 months. Fixed term contract posts are available for the stated period in the first instance but in particular circumstances may be renewed or made permanent subject to availability of funding.

What we offer:

Beyond a competitive salary, the University offers an attractive benefits package including a holiday entitlement of up to 8.4 weeks a year, pension schemes and development opportunities. We support staff wellbeing with flexible working options, work-life balance initiatives and support for physical and mental health. You can find more detail on all of this and more on our website.

Queen's University is committed to promoting equality of opportunity to all. We subscribe to Equality Charter Marks such as the Diversity Charter Mark NI and Athena Swan and have established staff networks such as iRise (Black, Asian, Minority Ethnic and International Staff Network) and PRISM (LGBTQ+) which help us progress equality. For further information on our commitment to Equality, Diversity and Inclusion, please visit our website.

Advert information