Research Fellow in Applied AI and Cybersecurity

The job requirements are detailed below.

Job title Research Fellow in Applied AI and Cybersecurity Job reference BEDS
2978 Date posted 23/10/2026 Final date to receive applications 15/11/2026 Location Luton Salary Grade 7 £ 40,704 - £48,337 Contract Full time Fixed Ter Contractual hours 35 Basis Full time Job category/type Research Attachments Blank Job information

The University of Bedfordshire’s Luton AI initiative is seeking a talented Research Fellow in Applied AI and Cybersecurity to develop new approaches to identifying unmanaged wireless and Internet of Things (IoT) security threats.

The post forms part of SAZT-Wi-Fi (Spatial-Aware Zero-Trust Platform for Shadow Wi-Fi IoT Threat Detection), an Innovate UK-funded project developing a standalone, plug-and-play cybersecurity proof of concept for critical infrastructure.

Luton AI is the University of Bedfordshire's growing applied AI research and innovation initiative, connecting researchers, engineers and industry partners to solve real-world problems. Since 2024, we have engaged more than 600 organisations and undertaken over 60 bespoke AI projects, spanning aviation, public services, cybersecurity and critical infrastructure. Our expanding AI computing and cybersecurity facilities support the development of new technology, from early research to working demonstrators.

You will join a collaborative, interdisciplinary team focused on turning ambitious technical ideas into useful, deployable solutions.

Working with Luton AI academics and industry partners, you will lead day-to-day technical development and integration of a working proof of concept combining Wi-Fi radio-frequency (RF) telemetry, machine learning, cybersecurity software and locally deployed AI for human-readable threat explanations.

You will translate research into a functional demonstrator, with an emphasis on robust software, reproducible experimentation and evaluation in realistic environments.

• Capturing and processing Wi-Fi RF metadata and network telemetry through passive monitoring and commercially available sensing technologies.
• Building data ingestion, processing and analysis pipelines for wireless telemetry.
• Designing, training and evaluating machine learning methods to detect anomalous, unmanaged or potentially malicious IoT activity.
• Combining spatial, behavioural, RF and network features, with local large language models to explain detected threats.
• Developing Python-based services, APIs, an accessible dashboard and interfaces with SIEM/SOC security tooling.
• Testing, benchmarking and refining the integrated proof of concept, documenting performance and limitations.
• Collaborating with academic and industry partners and contributing to reports, demonstrations, technical documentation and research or IP outputs.

We are looking for a technically capable, self-directed researcher who enjoys developing working systems.

You will have:

• A good honours degree, or equivalent professional experience, in Computer Science, Cybersecurity, AI, Data Science, Electronic Engineering or a related discipline.
• Strong Python software development skills and knowledge of machine learning for anomaly detection or classification.
• An understanding of Wi-Fi/network telemetry and cybersecurity, with practical experience analysing wireless or network data.
• Experience developing end-to-end data or software prototypes and evaluating models on real-world data.
• The ability to manage technical delivery, document findings and collaborate with academic and external partners.

Experience with local LLMs, edge deployment, RF sensing, Linux, containerisation, APIs or SIEM/SOC integration would be advantageous.

For an informal discussion, please…