Deep Learning and Behaviour Science for Secure Networked Autonomous Systems PhD

This is a funded PhD studentship open to all applicants with a fixed fee-stipend cap. The PhD studentship is a full-time 3-year position to support the strategic EPSRC grant – trustworthy autonomous systems: security. 

You will provide foundational research to support two of the investigators: Prof. Weisi Guo (AI, Networking), and Dr. Lisa Dorn (Human Behaviour). The key areas of research are security for AI and communications, and user behaviour. We seek students with a computer science or electronic engineering or informatics background, with a passion for AI, informatics, and security.

The world is automating and human trust for autonomous machines is pivotal in the social transition. In response, the UK government has invested £33m into fundamental research for trustworthy autonomous systems (TAS) across multiple universities – one of the largest research ecosystems in the world. Cranfield University is co-leading one of the research nodes in security.

The aim is to develop AI algorithms and behaviour science theory to improve the security of communications in autonomous systems. Of particular interest are adversarial attacks against AI algorithms and end-user behaviour adaptation in response. This will involve both algorithm development, experimentation in the field, and working with human end-users to identify cross-disciplinary socio-technical challenges.

This PhD studentship is funded by Cranfield to support the EPSRC node and this large emerging research area. You will be working inside the new £67m Digital Aviation Research & Technology Centre (DARTeC) and be part of the School of Aerospace, Transportation & Manufacturing (SATM) – a top 50 global faculty in aerospace and mechanical engineering (QS).

The algorithms developed in the PhD will be demonstrated on air and ground autonomous experimental platforms, working with our partners Lancaster University and other TAS institutions. The platforms will be used by academic collaborators and industrial end-users to test and understand emerging challenges and solutions, as well as demonstrate fundamental excellence.

The student will work with a large team of 4 research fellows and 1-2 other PhD students, with access to the aerospace test facilities at Cranfield (airport, UAVs, test track, radar, HPC, data…. etc.) and a large wider academic support network. Costs to conduct experimentation, travel to meet UK and international partners, and present at conferences, are all included. 

The experience of being to work on cutting edge AI and networking capabilities for secure autonomous systems will be attractive in both academic and industrial world, especially with some of our leading industrial partners. You will be supervised by Professor Weisi Guo (Professor of Human Machine Intelligence, former Turing Fellow), and Dr. Lisa Dorn (human behaviour adaptation) – a unique physical and social science learning environment.

Entry requirements

Applicants should have a first or second class UK honours degree or equivalent in a related discipline. This project would suit computer scientists, electronic engineers, and information scientists with hands-on experimentation experience. Students who are from underrepresented groups and diverse social-economic backgrounds are encouraged to apply, and if in doubt, discuss their application beforehand.

Funding

This studentship opportunity is open to applicants in the UK, EU and International. There are no restrictions on nationality. The PhD is fully-funded for UK applicants and partially funded for EU and International applicants. Please contact us for further information.

About the sponsor

Sponsored by Cranfield University (SATM Centre), this studentship will provide a bursary of up to £27,192 per year max including travel and other costs, depending the successful applicant. Please contact us for further information. 

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:

Name: Professor Weisi Guo

Email: Weisi.Guo@cranfield.ac.uk

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