KTP Associate (Computer Vision & Machine Learning Specialist)

Robert Gordon University; Aberdeen City; On-site; GBP 30,000 - 38,000; Full time

Benefits offered by this job

Personal development budget

Job summary

Robert Gordon University in Aberdeen invites applications for a Computer Vision & Machine Learning Specialist as a Knowledge Transfer Partnership (KTP) Associate. The role spans 36 months, a collaboration between AISUS and RGU, funded by Innovate UK and AISUS Offshore Limited.

You will base the work at AISUS’ and RGU’s premises in Aberdeen, focusing on AI-driven asset management for industrial inspection.

Qualifications

• First-Class Honours degree in Computing, Computer Vision, ML, AI or related field.
• Postgraduate qualification (MSc/PhD) desirable.

Responsibilities

• Develop a time-aware asset management and inspection solution.
• Predict asset degradation and remaining useful life.
• Transform complex inspection data into actionable insights.
• Collaborate with AISUS and Robert Gordon University on an industrial project.
• Utilise training opportunities and Personal Development Budget of £6,000.

Skills

Python

Deep Learning

Machine Learning

Computer Vision

Education

First-Class Honours degree in Computing/related

MSc or PhD in a relevant field

Tools

PyTorch

Job description

Job Summary

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 https://www.aisus.co.uk/ 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.

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.

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.

You must hold at least a First-Class Honours degree in Computing, Computer Vision, Machine Learning, Artificial Intelligence, or a closely related discipline. A postgraduate qualification, such as an MSc or PhD in a relevant field, would be highly desirable. You will be self-motivated, able to work independently and effectively within a small, dynamic team, and capable of delivering high-quality work to tight deadlines. You should demonstrate a genuine enthusiasm for applying advanced AI and machine learning methods to challenging real-world problems. Strong programming skills, particularly in Python, are essential. You must have practical experience with modern deep learning frameworks, such as PyTorch, together with strong applied knowledge of computer vision, machine learning and deep learning.

Excellent communication and interpersonal skills are required, as you must be able to communicate effectively with a range of different individuals. i.e., technical, academic, business and customers. Team working and flexibility will be a key requirement. You must be innovative, driven and willing to learn new skills.

Informal enquires may be sent to: Prof Eyad Elyan or the company supervisor Mr. Graeme McNay

Sponsorship is not available for this role but we will consider eligibility under the Global Talent visa.