Postgraduate research opportunities Cobot-enabled nondestructive quality control in production lines with advanced vision and machine learning for smart manufacturing
ApplyKey facts
- Opens: Tuesday 3 June 2025
- Deadline: Tuesday 15 July 2025
- Number of places: 1
- Duration: 3 years
- Funding: Home fee, Stipend
Overview
The overall research aim of this project is to fundamentally investigate the novel Cobot-enabled nondestructive quality control strategies by enabling holistic control and interactions of collaborative robots in complex human-robot collaborative manufacturing environments such that safety and interaction of nearby humans/operators can be maintained while the in-process inspection and quality control are being done.Eligibility
We are looking for you to have:
- a first class or upper second-class UK Honours degree, preferably a relevant Master degree with Distinction or international equivalent, in robotics, artificial intelligence, computing science, machine vision, electronic engineering, mechatronics and automation or a closely related field.
- a strong academic motivation and genuine research interest in robotics, artificial intelligence, and computer vision, as demonstrated by previous degrees, projects and/or work experience
- good software programming skills such as C/C++, Python, MATLAB etc
- good knowledge of robotics, human-robot collaboration, computer vision, machine learning or a strong willingness to learn quickly
- a collaborative mindset and an independent working style
- strong interpersonal skills with a focus on effective communication (written and oral) in English
- ability to learn how to learn
If English isn't your first language, you'll need an IELTS score of 6.5 or equivalent with no individual score below 5.5.

Project Details
Within the context of smart manufacturing or Industry 4.0, a strong technologic shift has become apparent within industry to move towards both more intelligence and more sustainability towards net-zero or zero waste targets. The importance of quality control in smart manufacturing process has always been recognized. However, now more than ever before, it is a key requirement in order for manufacturing companies to remain competitive and sustainable in the digital age. Because of the complexities and globalization of the manufacturing supply chain, real-time product quality control has become an important issue in the global manufacturing industry. Therefore, the fundamental understanding of the strategic application of how net-zero or zero waste ambition into practical smart robotic manufacturing is essential, especially for online quality control through in-process nondestructive inspection. To achieve the UK’s ambitious net-zero carbon objectives, research and innovation are required to solve a wide range of technological and operational challenges. In particular we are committed to address the Engineering Net Zero and Manufacturing and the Circular Economy challenges.
Though nondestructive techniques have been widely applied in manufacturing for many tasks including inspections, they are currently facing many emerging challenges arising from human-robot collaborative manufacturing processes, which have presented a fundamental knowledge gap on how to intelligently control product quality in-process by dealing with process uncertainties, variations caused by the highly customised production, dynamic interactions of humans/other machines including other robots and the need for greater flexibility and intelligence in smart manufacturing systems. Cobot-enabled nondestructive quality control in production lines with advanced vision and machine learning inspections provides a feasible solution to solving these challenges.
The ambition of this project is to fundamentally investigate novel human-robot collaborative nondestructive strategies for in-process quality control in smart manufacturing to fill the knowledge gap and research need identified above. The key novelty is the development of explainable machine learning algorithms, fast-response sensing (in particular machine vision and Nondestructive Testing (NDT)) and novel quality control strategy that implicitly consider human knowledge, process uncertainty, dynamic interaction and variation through intelligent fusion of multiple sensors and sources of information when making collaborative decisions for in-process quality inspection and control.
Funding details
This PhD project is funded by the John Anderson Research Studentship Scheme (JARSS). It covers UK home tuition fees and an annual tax-free stipend. International applicants are strongly encouraged to apply and to seek funding to cover the difference between the home and international tuition fees. Additional funding may be available to cover travel to conferences and academic events, software and equipment costs.
Home Students
To be eligible for a fully funded UK home studentship you must:
- Be a UK national or UK/EU dual national or non-UK national with settled status / pre-settled status / indefinite leave to remain / indefinite leave to enter / discretionary leave / EU migrant worker in the UK or non-UK national with a claim for asylum or the family member of such a person, and
- Have ordinary residence in the UK, Channel Islands, Isle of Man or British Overseas Territory, at the Point of Application, and
- Have three years residency in the UK, Channel Islands, Isle of Man, British Overseas Territory or EEA before the relevant date of application unless residency outside of the UK/ EEA has been of a temporary nature only and of a period less than six years
While there is no funding in place for opportunities marked "unfunded", there are lots of different options to help you fund postgraduate research. Visit funding your postgraduate research for links to government grants, research councils funding and more, that could be available.
Apply
Number of places: 1
Interviews with qualified and promising candidates will be conducted on a rolling basis until the position is filled.
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Design, Manufacturing and Engineering Management
Programme: Design, Manufacturing and Engineering Management