KTP Associate - Digital Manufacturing Engineer for AI based Quality Inspection

The Methodist Church in Britain; East Midlands; On-site; GBP 34,000 - 42,000; Full time

Job summary

Eminox partners with the University of Lincoln for a 24-month Knowledge Transfer Partnership to develop an AI-enabled vision system that detects defects in Diesel Particulate Filter units and predicts their performance in production. The associate leads the technical build and deploys it in live manufacturing environments.

Eligible candidates hold a Master’s in engineering or computing and have hands-on experience in machine learning, computer vision and data analytics, with strong collaboration

Qualifications

• Master's degree in engineering/computing discipline is essential.
• Experience in ML, CV and/or data analytics.
• Experimental work and quality-inspection techniques or systems.
• Researching and developing new or improved engineering systems or methods.

Responsibilities

• Develop machine-vision methods for defect detection and classification.
• Design and train AI models, including computer vision and deep-learning models.
• Capture multi-modal industrial datasets and establish data pipelines.
• Develop intelligent methods to predict downstream performance of DPF units.
• Integrate AI algorithms with manufacturing processes.
• Build prototypes and conduct system-integrated testing.
• Validate and optimise the system under industrial conditions.
• Lead technical milestones and communicate results.
• Translate research approaches into commercially viable solutions.
• Contribute to research outputs, reports, and publications.

Skills

Machine learning

Computer vision

Data analytics

Python

Mathematics

Critical thinking

Project management

Independent working

Education

Master's degree in mechanical engineering, manufacturing engineering, robotics, AI, computer science, data science or closely related discipline

Tools

OpenCV

PyTorch

TensorFlow

MATLAB

Job description

This is a 24-month Knowledge Transfer Partnership associate post linking Eminox, which makes exhaust after-treatment systems, with the University of Lincoln's CALIX automation lab. The work centres on an AI-enabled vision system that finds defects in Diesel Particulate Filter units and predicts how they will perform later in production. Its distinct feature is that the associate leads the technical build and deploys it in a live manufacturing environment, not only in a lab. A master's degree in an engineering or computing discipline is essential.

What the job involves

• Develop machine-vision methods for defect detection and classification
• Design and train AI models, including computer vision and deep-learning models
• Capture multi-modal industrial datasets and establish industrial data pipelines
• Develop intelligent methods to predict downstream performance outcomes of DPF units
• Integrate AI algorithms with manufacturing processes
• Build prototypes and carry out system-integrated testing
• Validate and optimise the system under industrial operating conditions
• Lead technical milestones and communicate results
• Translate research approaches into commercially viable solutions
• Contribute to research outputs, technical reports, publications and conference presentations
• Develop documentation and training materials so capability stays within Eminox

Qualifications

• Master's degree in mechanical engineering, manufacturing engineering, robotics, AI, computer science, data science or a closely related discipline

Experience

• Machine learning, computer vision and/or data analytics
• Experimental work and quality-inspection techniques or systems
• Researching and developing new or improved engineering systems or methods

Skills & knowledge

• Python or similar programming
• Strong mathematical, critical-thinking and problem-solving skills
• Analysing complex data and translating technical concepts into industrial solutions
• Managing a complex, multi-stakeholder project against demanding milestones
• Working proactively and independently, and collaborating with academic and industrial teams

Nice to have

• PhD in a relevant discipline
• Deep-learning frameworks such as PyTorch or TensorFlow
• Computer-vision tools such as OpenCV
• MATLAB or similar environments
• Familiarity with industrial datasets and manufacturing environments
• Process automation and control systems such as PLCs
• Industry 4.0 technologies or automated quality-inspection systems

What’s offered

• Direct mentorship and joint supervision from Eminox senior engineers and University of Lincoln academics
• Dedicated KTP personal development and training budget
• Nationally recognised management and leadership development through the Innovate UK KTP programme
• Opportunities to produce research outputs and engage with the wider research community
• Development of project-management, leadership and stakeholder-engagement skills
• Potential for continued career development within Eminox after the KTP