This is an exciting opportunity for a fully-funded PhD studentship in the Centre for Autonomous and Cyber-Physical Systems at Cranfield University, in the field of the explainable Artificial Intelligence (XAI) in radar systems for Air Traffic Management. This PhD investigates and develops Deep Learning classification and explainability methods that can be applied to a safety-critical radar system. This research is sponsored by EPSRC and SAAB UK under the Doctoral Training Partnership Funding 2020/21. The studentship will provide a bursary of up to £18,000 (tax free) plus fees* for three years. Read more Read less

Artificial Intelligence (AI) in civilian Air Traffic Management (ATM) is still in its infancy. Increased number of Unmanned Autonomous Vehicles (UAV) threatens the safety of both low-flying passenger jets and airports. Currently, most airport Primary Surveillance Radars (PSR) used for Air Traffic Control (ATC), do not typically perform real-time classification of aircraft radar signatures.

This PhD proposes the incorporation of the deep learning (DL) architectures in the classification of air vehicles radar signatures as an automated way of mapping these signatures to discrete aircraft classes. Here, the research will create real-time explainable AI (XAI) solutions ranging from data feature based to symbolic based to explain the DL actions.

Cranfield is an exclusively postgraduate university that is a global leader for education and transformational research in technology and management. It is the only Airport that has its own commercial Airport, controllers, commercial pilots and aircraft. This PhD will be hosted by the Centre for Autonomous and Cyber-Physical Systems and will be based at DARTeC which is a £65 million new research centre focused on the “Aviation of Future”. Cranfield is also setting up 16 km long national facility for drone flights (referred to as drone corridor) which will be extensively used for experimentation in this project. The Centre for Autonomous and Cyber-Physical Systems is one of the world’s largest centres of postgraduate education and research, with over 200 MSc and PhD students. In terms of facility, Cranfield University has a range of specialist research facilities available for different research activities (e.g. MUEAVI-multi-user environment for autonomous vehicle innovation facility). The facility operates as a collaborative and flexible space with specialist equipment available for indoor/outdoor flight tests for UAS systems. Also, the Centre for Autonomous and Cyber-Physical Systems offers the environment for algorithm development and simulation. Also, the centre can offer support, assistance with analysis, and method development for research.

The PhD will demonstrate how Deep Learning (DL) classification and explainability methods can be applied to Air Traffic Management, a safety-critical application. The implementation of these DL-based techniques within a safety critical system should ensure that the rationale behind the decisions made is adequately explained to the operator so that trust is maintained throughout the process and legally defendable.

You will be encouraged and supported in publishing own work in high quality peer-reviewed journals. Also, you will have opportunities and supports to present their work at relevant UK and international conferences. With the industrial collaborations with major radar manufacturer, you will have the opportunity to spend time with our industrial partners and learn from the professionals working on the development of cutting-edge technologies.

This is a very exciting project for a suitable candidate where you will be exposed to latest technological developments, learn from the industrial and academic experts working in this area and prepare for an exciting career in academia or industry.

At a glance

  • Application deadline26 Jun 2020
  • Award type(s)PhD
  • Start date28 Sep 2020
  • Duration of award3 years
  • EligibilityUK
  • Reference numberSATM155


1st Supervisor: Dr Dimitrios Panagiotakopoulos

2nd Supervisor: Prof Weisi Guo

Entry requirements

Applicants must have a first or second-class degree, and a Master’s degree, in engineering or a related informatics or computer science area. This project would suit someone with a strong background in computer programming, signal/image-processing (e.g. classification algorithms) and hands-on approach to systems integration and out of the box thinking ability.


To be eligible for this funding in full, applicants must be a UK national or have a permanent residence in the UK.

Due to funding restrictions all EU nationals are eligible to receive a fees-only award if they do not have “settled status” in the UK.

About the sponsor

Sponsored by EPSRC, Cranfield University and SAAB UK, this studentship will provide a bursary of up to £18,000 (tax free) plus fees* for three years.

Cranfield Doctoral Network

Research students at Cranfield benefit from being part of a dynamic, focused and professional study environment and all become valued members of the Cranfield Doctoral Network. This network brings together both research students and staff, providing a platform for our researchers to share ideas and collaborate in a multi-disciplinary environment. It aims to encourage an effective and vibrant research culture, founded upon the diversity of activities and knowledge. A tailored programme of seminars and events, alongside our Doctoral Researchers Core Development programme (transferable skills training), provide those studying a research degree with a wealth of social and networking opportunities.

How to apply

For further information please contact:

Dr Dimitrios Panagiotakopoulos
T: (0) 1234 750111 Ext: 8072

If you are eligible to apply for this studentship, please complete the online application form.