PhD Studentship: Low-Power AI-Driven Resource Management for Enhancing Satellite Communication Link Reliability

PhD Studentship available in the EPSRC Centre for Doctoral Training in Robotics and AI for Net Zero (RAINZ)

This studentship is offered by the EPSRC Centre for Doctoral Training in Robotics and Artificial Intelligence for Net Zero Centre (RAINZ) which is a partnership between three of the UKs leading universities (The University of Manchester, University of Glasgow and University of Oxford). 

Robotics and Autonomous Systems (RAS) is an essential enabling technology for the Net Zero transition in the UK’s energy sector. However, significant technological and cultural barriers are limiting its effectiveness. Overcoming these barriers is a key target of this CDT.  The focus of the CDT’s research projects will be how RAS can be used for the inspection, maintenance, and repair of new infrastructure in renewables (wind, solar, geothermal, tidal, hydrogen) and nuclear (fission and fusion), and to support the decarbonization of existing maintenance and decommissioning of assets. 

Year 1: You will spend the first year of the CDT at The University of Manchester undertaking taught MSc studies and research training. 

Years 2 – 4: You will move to your host institute (University of Manchester, University of Glasgow, or University of Oxford) to undertake your PhD research. 

About this Project  

Project Title: Low-Power AI-Driven Resource Management for Enhancing Satellite Communication Link Reliability

Supervisors: Dr Kevin Worrall  & Dr Yao Sun

Year 1 MSc Course: MSc Communication and Signal Processing

Year 2 – 4 PhD Location: Glasgow University

Research Abstract: This project aims to enhance satellite communication link reliability by developing low-power AI-driven solutions that detect potential link blockages and disruptions in real time, operating within the strict power constraints of satellite systems. Low-power AI is crucial in this context, enabling continuous link monitoring and decision-making without exhausting limited satellite energy resources. The AI models will predict potential failures and optimise communication strategies for robust and energy-efficient resource management.

Applicants should have a First or strong Upper Second-class honours degree (2:1 with 65% average), or international equivalent, in Engineering, Computer Science, Physics or Mathematics with evidence of programming experience.

Equality, diversity and inclusion is fundamental to the success of RAINZ CDT and is at the heart of all of our activities. We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, neurodiversity, ethnicity, gender, gender expression, sexual orientation, and transgender status.  We also support applications from mature students who are returning from a career break. 

Informal enquiries can be made by emailing rainz@manchester.ac.uk. 

Funding notes: Funding for this RAINZ CDT studentship is provided by the EPSRC and an industrial partner. The successful candidate will receive an annual stipend at the UKRI rate (£20,780 for 2025/26) and tuition will be paid at the home rate.

This project is subject to funding being confirmed by the industry partner.

Application Deadline: 17:00, 11^th July 2025. 

Start Date: 22^nd September 2025 

Advert information