Improving satellite communications using AI

Spiking Neural Networks (SNN) are an energy efficient alternative to Artificial Neural Networks (ANN). Low Earth Orbit (LEO) satellites could particularly benefit from using SNNs to perform Artificial Intelligence (AI) workloads in an energy-efficient manner. Availability of off-the-shelf components for assembling satellites and lower costs for launching have increased usage of LEO satellites for applications like climate monitoring, search-and-rescue, satellite broadband. Many of these applications rely on onboard deployments of AI which can impose a significant burden on the power resources of a satellite. This project will focus on developing efficient SNN-based techniques and demonstrate their effectiveness for applications onboard LEO satellites. 94% of Loughborough’s research impact is rated world-leading or internationally excellent. REF 2021

The standard University IELTS English language requirement is 6.5 overall with 6.0 in each individual element (reading, writing, listening and speaking).

International students: £29,500

UK students: £5,238

UK/Home: £5,238

International: £29,500

April 2026, July 2026, October 2026

31 July 2026

• Loughborough, United Kingdom