Graduate Teaching Assistant (GTA) - Federated Learning-based Intrusion Detection System for Intelligent Transportation Systems with Mixed Urban-Road Users

We are pleased to announce a fantastic opportunity for ambitious computer scientists to join our Computer Science Graduate Teaching Assistant (GTA) Programme!

How does it work?

Candidates will study for a four year, full time funded PhD (3 quarters of your time) whilst working and receiving a salary to gain valuable teaching experience (1 quarter of your time). Candidates will receive a salary and stipend package that exceeds the standard UKRI stipend for a full-time PhD.

Home/RoI Students will have their PhD fees waived, International students will receive a fee waiver equivalent to the Home/RoI fee and will be expected to fund the difference between the International fee and the Home/RoI fee. There will be a package of support to enable you to develop a research career in this exciting field.

PhD Topic: Federated Learning-based Intrusion Detection System for Intelligent Transportation Systems with Mixed Urban-Road Users

This research aims to develop a Federated Learning-based IDS to secure Intelligent Transportation Systems, addressing the unique challenges of mixed urban road users. By leveraging Federated Learning, the IDS ensures privacy-preserving, real-time detection of cyber threats in a multi-user environment. The system will integrate with the IDS tool to provide advanced intrusion detection capabilities, safeguarding connected transportation networks against evolving security threats. There are three main objectives:

1. Develop a Federated Learning-based Intrusion Detection System (IDS) tailored to Intelligent Transportation Systems (ITS) that can manage the diverse and dynamic environments of mixed urban-road users.
2. Enhance the security of Federated Learning models to protect the federated learning process from adversarial attacks, ensuring user data privacy.
3. Optimize the integration of heterogeneous data sources (e.g., vehicle telemetry, traffic cameras, sensors) within the Federated Learning framework to improve accuracy and scalability.

You will need to demonstrate you:

• Meet the academic requirements for a PhD offer from the University of Reading.
• Have a good (1st or 2.1) first degree in Computer Science, Statistics, Data Science, Mathematical Science, Meteorology, Physics or closely related subjects.
• Are able to effectively organise your time and prioritise tasks to balance PhD studies with GTA responsibilities.
• Are able to demonstrate scholarship in developing a publication record in your area of specialist expertise and conduct high quality PhD research.
• Are able to communicate scientific concepts clearly and with enthusiasm and in a way that engages students.
• Have good interpersonal skills and be able to work as part of a team.

How do I apply?

You must upload a combined CV and Proposal in pdf format (max size 1 MB) and complete the supporting statement.

Closing date: 04/042025

Interview: week commencing 21/04/2025

We look forward to hearing from you!

Contact details for advert

Contact Name Dr Zahra Pooranian

Contact Job Title Lecturer in Computer Science

Contact Email address z.pooranian@reading.ac.uk

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