KTP Associate – Safety Assurance of AI-based Energy Sector Applications

The main purpose of this role is to undertake and deliver the active research of the development, validation and industrial embedding of AI safety case methodologies for safety‑critical applications in the energy sector.

• Translating advanced academic research in AI assurance into practical, evidence‑based approaches applicable to real industrial systems engineering and assurance contexts.
• Lead the multidisciplinary research on structured safety arguments and associated evidence frameworks for AI‑enabled systems, taking account of the distinctive challenges posed by non‑deterministic behaviour, uncertainty, and human–AI interaction.
• Disseminating outcomes to the wider DNV UK team through artefacts, documentation, and tutorials
• Contribute to the CfAA and wider research community of Safe AI, which includes publishing research outcomes at top conferences and journals.

Key responsibilities

(Role holders will be required to undertake some or all of the duties below)

• To lead the technical delivery of the KTP work plan, including the definition of use cases, development of AI safety cases, and delivery of agreed project outputs.
• Conduct innovative research on the design, adaptation, and application of AI risk assessment and assurance methodologies, including safety arguments, evidence collection strategies, and validation
  metrics for AI-enabled systems in the energy sector.
• Support real-world testing and validation of AI systems, including engagement with DNV’s Spadeadam test site and other controlled environments as required.
• Ability to conduct collaborative multidisciplinary work, which includes DNV engineers, data scientists, and assurance specialists, to ensure methodologies are practical, scalable, and aligned with industrial and regulatory needs.
• Liaise with various DNV teams to produce technical documentation, guidance material, templates and tools to support long-term embedding of AI assurance within DNV.
• Present clearly project progress and findings at KTP Local Management Committee meetings, technical meetings and stakeholder workshops, including preparation of written progress reports, and proposed future plans.
• To assist with DNV team members and the CfAA, in their own area of expertise, through meetings, producing publications, and coordinating the work of small research teams
• Manage the KTP budgets provided and efficiently deploy resources to meet project milestones
• Actively demonstrate a commitment to professional development by continuing to advance knowledge, understanding and competencies.

The above list of duties is not exhaustive and is subject to change. The post holder may be required to undertake other duties within the scope and grading of the post.

The project will develop and embed a structured, scalable AI safety case framework within DNV’s assurance services. This framework will extend established safety‑engineering and systems assurance principles to AI‑enabled systems, explicitly addressing challenges such as uncertainty, non‑deterministic behaviour, human– AI interaction and regulatory compliance. The work will be grounded in real industrial use cases from the energy sector, with validation activities supported by DNV’s testing capabilities, including access to controlled environments such as the Spadeadam test site.

The KTP Associate will be embedded within DNV’s Digital Energy team in Glasgow, working day‑to‑day in aprofessional “industry” environment (office-based) while maintaining close academic links with the CfAA. Through this embedded model, the Associate will act as the primary conduit for knowledge exchange between academia and industry, ensuring that academic AI assurance methodologies are translated into practical, scalable tools and processes that can be sustained within DNV beyond the lifetime of the project. To facilitate this transfer, the KTP associate will actively engage with academics from the CfAA, with planned visits to the CfAA during the project as further support. The role offers the opportunity to work at the interface of applied research and industrial delivery, contributing directly to the safe deployment of AI in critical national energy infrastructure.