Cybersecurity and Machine Learning Engineer (KTP Associate)

The candidate should have PhD-level training or strong / equivalent work experience on cybersecurity or a similar background, with evidence of designing new relevant products and working with big and diverse teams. They will need to have advanced-level knowledge of power grid security and machine learning concepts with proficient coding skills (eg MATLAB, Python, or similar) and power grid simulation software (eg PSCAD, Power Factory, etc). The Associate must also learn to integrate the machine learning backend of the intrusion detection system to real-world substation setups. This requires development of specialised APIs and lifecycle management of the machine learning model.

This role will require a strong understanding of electrical engineering and power systems principles in order to encourage communication with a variety of stakeholders, including IT&D specialists and energy engineers.

You are a proactive and self-motivated professional who thrives working both independently and as part of a team. You are confident managing your own workload with minimal supervision, consistently using initiative to identify improvements, solve problems, and drive tasks through to completion.

You bring experience in project management, with the ability to plan, coordinate, and deliver work effectively.

An excellent communicator, you have strong written and verbal English skills and are comfortable engaging with a wide range of stakeholders. Your interpersonal skills enable you to build positive working relationships and collaborate effectively across teams.

You are also committed to continuous professional development and actively seek opportunities to enhance your skills and knowledge.

The Associate will be employed on a Knowledge Transfer Partnership (KTP) project between the University of Warwick and National Grid. The 29 months project will deliver a specialised cybersecurity strategy to be rolled out across National Grid’s intelligent substations. The skills required are multi-disciplinary, with knowledge of power system security and machine learning being crucial.

The Associate will primarily work alongside National Grid engineers to integrate the machine learning backend of the intrusion detection system to real-world setups.

This post is part of the Knowledge Transfer Partnership (KTP) initiative funded by Innovate UK. Knowledge Transfer Partnerships is a UK-wide programme that has been helping businesses for the past 50 years to improve their competitiveness and productivity through the better use of knowledge, technology and skills that reside within the UK Knowledge Base.

The skills the Associate will develop will put them at the cutting edge of power grid cyber-physical security, equipping them to address emerging threats, design resilient architectures, and safeguard critical energy infrastructure. They will collaborate with a dynamic team of National Grid staff and University of Warwick academics to achieve the project goals.