Fully Funded PhD Studentship: Wireless Communication, Signal Processing, and AI
Fully FundedManchester, United Kingdomphd· £21,805 per year (tax-free, UKRI rate for 2026/27)
Project Overview
This fully funded PhD explores AI-native and sensing-aware wireless systems where communications and sensing are co-designed end-to-end.
Basic Information - Full Project Title: PhD Studentship: Wireless Communication, Signal Processing, and AI - Host Institution: The University of Manchester - Department: Department of Electrical and Electronic Engineering - Supervisor: Dr Kaitao Meng - Study Level: PhD - Duration: 3.5 years - Start Date: January 2027 - Funding Type: Fully Funded - Stipend: £21,805 per year (tax-free, UKRI rate for 2026/27)
This fully funded PhD explores AI-native and sensing-aware wireless systems where communications and sensing are co-designed end-to-end. You will unify modern machine learning, statistical signal processing, or optimisation to turn heterogeneous knowledge (channel/network state, maps and topology, mobility, hardware constraints, and task-level KPIs) into reliable and efficient decisions. The work spans theory to lightweight on-hardware prototypes, with publications targeted at leading IEEE venues in communications and signal processing, and relevant AI venues.
Indicative Research Directions (choose one or combine):
- Wireless resource allocation and scheduling under multi-objective KPIs (rate, latency, detection, localisation, etc.)
- Reconfigurable/programmable radio environments and system/network-level antenna design
- Theory with guarantees (convex/non-convex optimisation, performance analysis, machine learning, etc.)
Requirements
- At least a Master's (or international equivalent) in a relevant science or engineering-related discipline
- Strong programming ability in optimisation or machine learning (e.g., Python/Matlab/C++; PyTorch/TensorFlow)
- Experience in signal processing/wireless or SDR/GPU prototyping is a plus
- Demonstrated research potential is highly desirable (evidence may include peer-reviewed publications in top-tier journals and conferences)
How to Apply
Contact the main supervisor, Dr Kaitao Meng (kaitao.meng@manchester.ac.uk). Please include details of your current level of study, academic background, any relevant experience, and a paragraph about your motivation to study this PhD project. Apply early as the advert will be removed once the position has been filled.
