PhD Studentship: Distributed Machine Learning over Mobile Networks

Cranfield University; England; On-site; GBP 20,000 - 24,000; Full time

Benefits offered by this job

Tax-free bursary

Fees paid for three years

Job summary

Cranfield University invites applications for a PhD studentship focused on AI-on-RAN, exploring distributed and mobile inference of large language models in future 6G networks. The project blends AI, wireless networking, and edge computing with exposure to national stakeholders.

The successful candidate will have a strong AI background and curiosity about wireless systems, supporting outcomes that shape next-generation AI algorithms and resource allocation in telecommunications.

Qualifications

• Applicants should have a UK honours degree or equivalent in a related discipline of computer science, statistics, or electronic / information engineering.
• This project would suit students with a fundamental curiosity to how foundation models work at the algorithmic level.

Responsibilities

• Conduct research on AI-on-RAN to optimise distributed and mobile inference of large language models.
• Explore token/memory/bandwidth management for real-time operation.
• Contribute to publications and engagement with UK stakeholders.

Skills

AI fundamentals

Edge computing

Research mindset

Education

UK honours degree in computer science, statistics, or electronic / information engineering

Job description

This is a project focused on how AI will provide a seamless service to mobile users of tomorrow. It is expected the student will have a background in AI with a curiosity on wireless networking.

Machine learning that occurs over a distributed network is attractive to reduce latency, ensure data and compute is sovereignly hosted, and utilise available edge computation resources. There are many challenges when the demand is spatially and temporally mobile, and when the inference is computationally heavy. This PhD studentship will advance the research in AI on Radio Access Networks (AI-on-RAN) by exploring how to optimise distributed and mobile inference of large language models by adapting how tokens, memory, and network bandwidth is used in real time.

The outcomes are expected to shape how future AI algorithms are designed, as well as how future computation and communication resources are invested in 6G telecommunication networks.

There is both university-based and wider FTH ecosystem training with other PhD students from other universities across UK. The experience gained is not only cross-disciplinary (AI and telecoms), but also there will be exposure to different stakeholders beyond these including defence, government, and infrastructure.

Supervisor

1st Supervisor: Prof. Weisi Guo

2nd Supervisor: Dr. Mengbang Zou

At a glance

Application deadline: 03 Feb 2027

Award type(s): PhD

Start date: 07 Jun 2027

Duration of award: 3 years (full-time)

Eligibility: UK

Entry requirements

Applicants should have a first or second class UK honours degree or equivalent in a related discipline of computer science, statistics, or electronic / information engineering. This project would suit students with a fundamental curiosity to how foundation models work at the algorithmic level.

We will take an inclusive approach during your recruitment process and consider students from underrepresented groups and different social economic groups.

Funding

Sponsored as part of the EPSRC Future Telecommunications Hubs (FTH) initiative and Cranfield University, this studentship will provide a bursary of up to £21,805 (tax free) plus fees for three years.

Eligibility for Home fee status is determined with reference to UK Department for Education rules. As a guiding principle UK or Irish nationals who are ordinarily resident in either the UK or Republic of Ireland pay Home tuition fees. All other students (including those from the Channel Islands and Isle of Man) pay Overseas fees. Further advice can be found on the UK Council for International Student Affairs (UKCISA) website.