PhD Studentship - Wireless Communication, Signal Processing, and AI

The University of Manchester - Electrical and Electronic Engineering

Qualification Type:PhDLocation:ManchesterFunding for:UK StudentsFunding amount:£20,780 - please see advertHours:Full TimePlaced On:2nd September 2025Closes:30th September 2025

This 3.5-year PhD studentship is open to Home (UK) applicants. The successful candidate will receive an annual tax-free stipend set at the UKRI rate (£20,780 for 2025/26; subject to annual uplift), and tuition fees will be paid. We expect the stipend to increase each year. The start date is January 2026.

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 directions (choose one or combine):

• Network-level design and multi-node cooperation (coordination, topology design, distributed/federated learning, etc.)
• 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.)

Applicants should have, or expect to achieve, 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 (e.g., IEEE Transactions/Letters) and top conferences.

To apply, please contact the main supervisor, Dr. Kaitao Meng - kaitao.meng@manchester.ac.uk. Please include details of your current level of study, academic background and any relevant experience and include a paragraph about your motivation to study this PhD project.