Research Fellow in Applied AI and Cybersecurity

University of Bedfordshire; Luton; On-site; GBP 41,000 - 48,000; Full time

Benefits offered by this job

Generous pension

Annual leave entitlement

Flexible working arrangements

Personal and professional development

Family-friendly policies

Job summary

The University of Bedfordshire in Luton invites applications for a Research Fellow in Applied AI and Cybersecurity to drive the development of a proof-of-concept project combining Wi-Fi telemetry, ML models and cybersecurity tooling. The role focuses on robust software, reproducible experiments and real-world evaluation.

You will collaborate with academics and industry partners, develop Python services and dashboards, and contribute to reports, demonstrations and IP outputs during a fixed-term

Qualifications

• 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.
• 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.
• 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.

Responsibilities

• Lead day-to-day technical development and integration of a working proof of concept combining Wi-Fi RF telemetry, ML, cybersecurity software and local AI.
• Develop data ingestion, processing and analysis pipelines for wireless telemetry.
• Design, train and evaluate machine learning methods to detect anomalous IoT activity.
• Develop Python-based services, APIs, an accessible dashboard and interfaces with security tooling.
• Test, benchmark and refine the integrated proof of concept with documentation of performance.

Skills

Python

ML for anomaly detection

Wi-Fi telemetry

Cybersecurity

Data pipelines

Linux

Containers

APIs

SIEM/SOC integration

LLMs / edge deployment

Education

Honours degree in Computer Science / Cybersecurity / AI / Data Science or related

Tools

Python

Linux

Docker

APIs

Job description

The job requirements are detailed below.

Job details

Job title Research Fellow in Applied AI and Cybersecurity Job reference BEDS2978 Date posted 23/10/2026 Application closing date 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.

About Luton AI

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.

About the role

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.

What you will be doing

• 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.

About you

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 contact Dr Edward Braund, Head of School and Director of Luton AI, on 01582 743209 or [e-mail]

The post will be based at Luton (with travel to other campuses as required)

Interview date is w/c 23rd Nov 2026.

The University is committed to equality of opportunity and values the diversity of all its workforce and student population.

At the University of Bedfordshire, inclusivity is a key University value, and we are committed to creating an environment in which we celebrate and value the diversity of our staff and student community and provide fair opportunity and outcomes for all.

We recognise that diversity fosters creativity and innovation, and we want our community to represent and benefit from opportunities that the diversity of talent and thought creates.

We, therefore, particularly encourage applications from candidates who are likely to be underrepresented in the University of Bedfordshire’s workforce. These include people from Black, Asian and Ethnic Minority backgrounds (particularly in roles in grades 7 and above), disabled people; LGBTQ+ people; and women in some senior grades.

The University has a responsibility to ensure that all employees are eligible to live and work in the UK.

We offer a range of employee benefits including a generous pension and annual leave entitlement, free fitness classes and gym membership, flexible working arrangements, personal and professional development and family-friendly policies.

Please note a Curriculum Vitae will only be accepted if accompanied by a fully completed University application form.