Computer Vision and Machine Learning Specialist (KTP Associate)

Computer Vision and Machine Learning Specialist (KTP Associate)

This is an exciting opportunity for an ambitious Computer Vision & Machine Learning Specialist to fast-track their career development as a Knowledge Transfer Partnership (KTP) Associate, utilising expertise in Machine Vision and Machine Learning, with a particular focus on Deep Learning and Modern AI Frameworks. You will undertake a 36-month collaborative project between AISUS and Robert Gordon University (School of Computing, Engineering and Technology), jointly funded by Innovate UK and AISUS Offshore Limited.

The post will be based at the company’s premises in Aberdeen. As a Computer Vision & Machine Learning Specialist, you will be responsible for developing a time-aware asset management and inspection solution that transforms complex inspection and operational data into actionable insights. The solution will predict asset degradation and remaining useful life, enabling proactive maintenance, optimising inspections, extending asset life, reducing unplanned downtime, and minimising material and operational waste to support safer, more efficient industrial operations.

Duties & Responsibilities

• Transfer knowledge and expertise in Computer Vision and state-of-the-art Machine Learning, with particular emphasis on AI-driven predictive maintenance, to AISUS Offshore Limited.
• Take a leading role in developing methods for the exploration, visualisation, pre-processing and management of large-scale, complex multimodal inspection datasets.
• Develop scalable, time-aware data pipelines for offshore assets, integrating multimodal inspection and operational data into a continuously evolving digital repository.
• Explore and develop advanced data representation and feature-learning methods to identify patterns of asset degradation and capture the progression of failure mechanisms over time.
• Implement, evaluate and benchmark advanced temporal AI methods, including state-of-the-art transformer-based sequence models, to predict time-to-failure and remaining useful life (RUL), with quantified uncertainty to support risk-based decision-making.
• Develop the technical, communication and professional skills required to take on increasing levels of responsibility throughout the project
• Deliver the project objectives as detailed in the KTP project workplan.
• Maintain an up-to-date project plan and provide regular progress reports.
• Deliver presentations to immediate project team members and other stakeholders.
• Any other duties that may be reasonable, assigned by the Academic Supervisor/ Company Supervisory teams.

You will receive extensive practical and formal training, gain marketable skills, broaden your knowledge and expertise within an industrially relevant project, and gain valuable experience from industrial and academic mentors.
You will also benefit from a Personal Development Budget of £6,000.

To develop a time-aware asset management and inspection solution that transforms complex inspection and operational data into actionable insights. The solution will predict asset degradation and remaining useful life, enabling proactive maintenance, optimising inspections, extending asset life, reducing unplanned downtime, and minimising material and operational waste to support safer, more efficient industrial operations.

About the business

AISUS specialise in offshore inspection services, pioneering new advanced inspection technology for the global energy industry. With a team of expert professionals and a deep understanding of offshore inspections, AISUS deliver high-quality services and support to their clients.

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