PhD: Novel Deep Learning Techniques for Non-Cooperative Geolocation in Urban Environments

at Cranfield University
Published November 14, 2022
Location Cranfield, United Kingdom
Category Deep Learning  
Job Type Scholarship  


Description –

The main purpose of the PhD is to work with our industrial partners at Plextek and leverage from the latest developments in state-of-the-art deep learning techniques in order to help address the problem of emitter geolocation in complex and cluttered environments. The intention is to develop methods that are more accurate than generic statistical approaches and of lower computational burden than approaches using numerical propagation methods.

Cranfield Defence and Security (CDS) provide unique educational opportunities to the Defence and security sectors of both public and private sector organisations.

Based at the UK Defence Academy at Shrivenham in Oxfordshire, CDS is the academic provider to the UK Ministry of Defence for postgraduate education at the Defence Academy, training in engineering, science, acquisition, management and leadership.

You will work on a cutting-edge research project in collaboration with Plextek which is expected to deliver high impact results.

You will have the opportunity to work closely with our industrial partners at Plextek and to travel for meetings with our industrial partner and to conferences to present your results.

You will gain strong independent thinking and research skills, both theoretical and experimental. A number of writing courses are available which will assist in report, scientific paper, presentation and thesis writing. You will gain experience in presenting work at workshops and conferences, and also in programming and signal processing. All of these are transferable skills enhancing employability.


Award Type – PhD

Start Date – 06/02/2023

Duration of Award – Full Time 3 Years 

Entry Requirements – Applicants should have a Master’s degree in an appropriate subject (Computer, Electrical or Electronic Engineering, Mathematics or Physics) and a 1st class or 2.1 UK Honours degree (or equivalent from an overseas university).

Funding – This studentship will provide a salary of £18,000 per annum (tax free) and will cover all registration fees for three years.

How to Apply –

For information about applications please contact: