Computer Vision Engineer – KTP Associate

COBA Holdings Limited and the College of Computing at Birmingham City University (BCU) are seeking to appoint a high-calibre graduate (graduated within the last five years) for the role of Computer Vision Engineer (KTP Associate).

This position is part of a Knowledge Transfer Partnership (KTP), co-funded by Innovate UK and COBA Holdings Limited, offering an exceptional opportunity to work at the intersection of cutting-edge research and industry application. The successful candidate will be employed by BCU and will work full-time on a 24-month project in partnership with COBA Plastics Group.

As the Computer Vision Engineer (KTP Associate), you will play a pivotal role in developing state-of-the-art computer vision and AI-driven quality assurance solutions for COBA’s manufacturing lines, ensuring 100% defect detection through automated inspection. This role provides significant industry exposure and direct influence across multiple business divisions, collaborating with COBA’s senior leadership, production teams, and academic mentors.

To support professional growth, the associate will benefit from a £4,000 Personal Development Budget to enhance their technical and leadership skills. Additionally, there is the potential to progress towards a higher learning degree at Birmingham City University aligned to the project ambitions, allowing the associate to build upon their project research and develop deeper expertise in AI and computer vision applications in manufacturing.

This is a career-accelerating opportunity for an ambitious graduate to gain high-level industry experience, contribute to real-world AI-driven automation, and develop commercially impactful research.

Candidate Requirements:

– A minimum 2:1 degree in Computer Science, Mathematics, AI, or a related STEM discipline (postgraduate qualifications are desirable)
– Strong technical programming and research capabilities, with experience in computer vision or AI
– Excellent communication and problem-solving skills, with an ability to translate research into real-world industry solutions
– A proactive mindset, keen to lead innovation within a business setting and influence strategic decision-making

Location and Reporting:

– This Knowledge Transfer Partnership (KTP) Associate position will be based at COBA Automotive, Marlborough Drive, Fleckney, Leicestershire, LE8 8UR
– The successful candidate will have full access to Birmingham City University’s resources such as offices, labs, and library to complete the KTP project (a workplan has already been written with KPIs and outcome deliverables)
– The Computer Vision Engineer (KTP Associate) will be supervised and mentored by both a lead academic and academic supervisor(s). Academics from BCU’s College of Computing within the Faculty of Computing, Engineering and the Built Environment (CEBE) as well as a company supervisor located at COBA Automotive who’s aim is to assist the Computer Vision Engineer to deliver the knowledge into COBA Holdings Limited and successfully deliver the 24-month KTP project on behalf of COBA Holdings Limited and Birmingham City University

Main Duties and Responsibilities:

– The Associate will develop computer vision solutions for quality assurance (QA) on COBA Automotive’s manufacturing lines, identifying defects and advancing COBA’s goal of 100%-part inspection
– The role requires the successful candidate to develop novel (QA) computer vision solutions for COBA. This will include producing camera systems to capture data from each part without interfering with production, capturing datasets of manufactured components and developing and training new machine learning (ML) approaches. To provide optimal solutions for COBA, the candidate will need to research and build upon current state-of-the-art methods
– The Associate will be expected to interact with colleagues at COBA as well as academic supervisors at BCU. Their work will involve disseminating and embedding the techniques developed at COBA, in addition to academic dissemination of their work via paper submissions and conference talks
– The Associate must also be adept at applying their knowledge to commercial projects, driving value and making an impact where possible, with an ability to solve problems and create innovative solutions

The Associate must have the following key attributes to ensure the project’s successful completion

Skills and Experience:

Essential:
– A minimum 2:1 undergraduate qualification, or a postgraduate qualification in Computer Science, Mathematics or a related STEM field
– Strong technical, programming, problem-solving and research capability, demonstrated in previous projects
– Excellent communication skills to express ideas effectively, orally, graphically and in writing to articulate complicated matters between the academics and the company project team members
– An ability to work to tight deadlines (with attention to detail) and maintain high standards of work
– An ability and aptitude to work effectively as part of an interdisciplinary team; and self-management and planning skills to make optimum use of time
– Strong leadership skills in successfully implementing and embedding new innovations within a company or organisation

Desirable:
– Proven academic or professional experience in developing computer vision solutions, particularly camera systems and/or neural network classifiers
– Experience with front and back-end web development
– Academic acumen to enable successful reporting through research publications in academic journals and marketing/training materials
– A driving license and a willingness to travel to utility contracts throughout the UK
– Relevant work experience, particularly involving computer vision and manufacturing

On a personal note, the Associate should be enthusiastic, motivated, punctual, conscientious, trustworthy and a good team worker.

The Project
The project aims to enhance COBA Automotive’s quality assurance (QA) capabilities through the development and deployment of novel computer vision (CV) systems. These systems will enable automatic inspection of 100% of parts, helping COBA to improve efficiency and ensure quality of all parts shipped to customers.

The CV systems will comprise both hardware and software. The associate will be responsible for developing camera systems tailored to the different manufacturing setups and components produced at COBA. They will also produce the software powering these systems, which is planned to involve collection of datasets and training of CNN-based classifiers to identify defective parts. Development of
both components will involve researching and building upon state-of-the-art approaches.

The Associate will also be responsible for embedding the knowledge developed in the project within the company (COBA), enabling them to continue to achieve 100% part inspection on future products. This will involve activities such as carrying out workshops and seminars within the company, and developing documentation and training materials. The associate will also have the opportunity to disseminate key findings in the project academically, for example through publication of papers and at academic conferences.