New continuous learning driven techniques for evolvable cross-layered autonomous networks

This PhD project will research into new technical innovations that can embrace today’s transfer machine learning advances into a process of designing and controlling cross-layered (i.e., PHY and MAC) networks with autonomous mobility nodes in a range of connected autonomous mobility applications. This will take into account online learning and autonomously networked communications. The main objective of this project is to develop new opportunities for applying continuous machine learning algorithms to cross-layered wireless networks, and the developed opportunities will be harmonised with several biologically inspired algorithms in order to unleash potentials of a network of autonomous mobility nodes, which will run in uncontrolled environment in a distributed fashion and rely more on a swarm of small data-agents rather than one big central agent. The project will challenge how emerging autonomous mobility nodes can be networked in a distributed manner and adapted in close proximity to data source to deployed environments.

Entry requirements:

Candidates should have (or expect to obtain) a minimum of a UK upper second class honours degree (2.1) or equivalent in Electronic and Electrical Engineering, Physics, Computer Science, Mathematics or a closely related subject.

How to apply:

Applicants should apply via the University’s online application system at https://www.york.ac.uk/study/postgraduate-research/apply/. Please read the application guidance first so that you understand the various steps in the application process.