George Goussetis

This fully funded PhD opportunity at Heriot-Watt University, supervised by Assistant Professor Mohd Tasleem Khan, Dr Elliot Crowley, and Prof George Goussetis, is part of the CDT Sensing, Processing and AI for Defence and Security (SPADS). The project focuses on developing high-performance kernel learning processors for next-generation sensing and 6G systems, integrating advanced machine learning, signal processing, and hardware design. Research will address the challenges of kernel-based online learning under the extreme data rates and dense environments expected in 6G, aiming to overcome computational bottlenecks through analytical modeling and hardware-efficient algorithm design. The work will involve designing and optimizing high-speed kernel learning algorithms that exploit sparsity, feature selection, and reduced model complexity for real-time sensor signal processing. Hardware-efficient architectures will be created and validated using representative 6G sensing data, with a focus on throughput, energy consumption, silicon area, and learning accuracy for ASIC deployment. The project is ideal for candidates with backgrounds in Electrical and Electronic Engineering, Computer Science, or related fields. Applicants should hold at least a 2:1 Honours degree (or international equivalent) in a relevant discipline, and an MSc is advantageous. A strong foundation in signal processing, machine learning, or hardware design is highly desirable. English language requirements apply for EU/Overseas applicants. Funding includes a stipend of £31,000 per year and coverage of home tuition fees. The application deadline is 31 January 2026. For more information and to apply, visit the project link or contact [email protected] for general enquiries. Keywords: kernel learning, 6G sensing, machine learning, signal processing, hardware design, ASIC, real-time processing, sparsity, feature selection, energy efficiency.