We recommend that you apply early as the advert will be removed once the position has been filled.

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.)

1) Applicants should have, or expect to achieve, at least a master’s (or international equivalent) in a relevant science or engineering-related discipline.

2) 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.

3) 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.

We recommend that you apply early as the advert will be removed once the position has been filled.

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.)

1) Applicants should have, or expect to achieve, at least a master’s (or international equivalent) in a relevant science or engineering-related discipline.

2) 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.

3) 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.