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.
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
Supervisor
- 1st Supervisor: Prof. Weisi Guo
- 2nd Supervisor: Dr. Mengbang Zou
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.
#J-18808-Ljbffr
PhD Studentship: Distributed Machine Learning over Mobile Networks in Cranfield employer: Cranfield University
Cranfield University is an exceptional employer, offering a vibrant research environment that fosters innovation and collaboration in the field of sustainable energy. With a commitment to employee development, you will have access to cutting-edge resources and mentorship opportunities within the Hydrogen Integration Incubator, making it an ideal place for aspiring researchers to thrive and contribute to meaningful advancements in carbon-free technology.
