Development of Adaptive Conversational VR-based Real-time Feedback System for Net-Zero Manufacturing Operations

About the Project

The Engineering and Mathematics Group within the School of Computing, Engineering, and the Built Environment is inviting applications for a Doctor of Philosophy (PhD) in developing an adaptive conversational VR-based real-time feedback system for net-zero manufacturing operations.

Accurate real-time performance assessment of a manufacturing factory is complex due to the multiple factors influencing production outcomes, including operator actions, materials, processes, equipment, and environmental conditions. With IoT commonly available in the industry, the research challenge posed by digital manufacturing is not the capture of data but rather the lack of computational methods to analyse large flows of diverse (i.e., multimodal) sensor data. Manufacturers are interested in developing data-driven tools and techniques to monitor manufacturing units in real-time and develop intelligent, proactive feedback strategies to improve performance. This Doctor of Philosophy (PhD) research project aims to develop conversational virtual reality (VR) based real-time system modelling techniques that proactively analyze multimodal sensor data, assess manufacturing factory performance, design feedback mechanisms for improvement strategies, and evaluate the impact of proposed improvements.

The project focuses on the following research objectives: (i) develop automated manufacturing process models based on multimodal factory data and integrate seamless to VR factory model (that includes integrated manufacturing system such as mobile robot, flexible manufacturing system and operators), (ii) develop real-time factory performance operational model through automated IoT data, (iii) develop VR enabled feedback mechanisms on performance assessment and improvements, and (iv) assess net-zero implications in the developed VR-enabled feedback system.

Since improving manufacturing factory economic and environmental performances is the core objective, this research requires an excellent understanding of manufacturing systems, system engineering principles, data analytics, and machine learning (i.e., predictive modelling) techniques. Furthermore, the research involves a complete data processing cycle, such as multimodal manufacturing data collection with appropriate sensors (e.g., workers' movement, machine temperature, and vibration), data integration, data cleaning, and data transformation. Therefore, it would be ideal if the candidate has experience in big data analytics or system simulation modelling software such as Siemens Plant Simulation and advanced programming skills.

The researcher joining this project will have the opportunity to work within the Flexible Manufacturing Laboratory at Edinburgh Napier University, gaining valuable experience and training in the appropriate technical areas. They will be actively encouraged to present their work at leading international conferences, enhancing their professional profile. The researcher will also benefit from collaborating with Professors at the University of Edinburgh and Strathclyde through the completed EPSRC (The Engineering and Physical Sciences Research Council, UK) funded research project (EP/V051113). This project offers a unique opportunity for a motivated and intellectually curious individual to make a significant contribution to the field of manufacturing systems.

Perspective applicants are encouraged to contact the Supervisor (Gokula Vasantha) before submitting their applications. When applying, please make it clear that you are applying for the 'VR-based real-time feedback system for net-zero manufacturing operations' project and include the names of the supervisors. We look forward to receiving your applications. Prospective applicants can review some of the following research papers from our group to familiarize themselves with this topic.

Academic qualifications

First degree (minimum 2:1 classification) in one of the following areas - Mechanical Engineering, Energy Engineering, Manufacturing, Computer Engineering, Mechatronics Engineering, Engineering Science, Data Science, Operational Science

English language requirement

IELTS score must be at least 6.5 (with not less than 6.0 in each of the four components). Other, equivalent qualifications will be accepted.

Essential attributes:

• Fundamental knowledge in the following areas: Data analytics, Manufacturing systems, Performance analysis, Operational research, Virtual Reality Applications, Large-language model
• Experience of fundamental manufacturing systems and processes
• Competent in data analytics and statistical techniques
• Knowledge of simulation processes and prediction approaches
• Good written and oral communication skills
• Strong motivation, with evidence of independent research skills relevant to the project
• Good time management

APPLICATION CHECKLIST

• Completed application form
• CV
• 2 academic references, using the Postgraduate Educational Reference Form (download)
• Research project outline of 2 pages (list of references excluded). The outline may provide details about
  1. Background and motivation of the project. The motivation, explaining the importance of the project, should be supported also by relevant literature. You can also discuss the applications you expect for the project results.
  2. Research questions or objectives.
  3. Methodology: types of data to be used, approach to data collection, and data analysis methods.
  4. List of references.
• Statement no longer than 1 page describing your motivations and fit with the project.
• Evidence of proficiency in English (if appropriate)

To be considered, the application must use

• the advertised title as project title

For informal enquiries about this PhD project, please contact g.vasantha@napier.ac.uk

PhD Start Date: October 2026

Funding Notes

International applicants should note that visa application costs and the NHS health surcharge are additional costs to be taken into consideration, and successful applicants will need to cover these expenses themselves.

References

Ayse Aslan, Gokula Vasantha, et al. Smarter facility layout design: leveraging worker localisation data to minimise travel time and alleviate congestion, International Journal of Production Research, 2025.
Ayse Aslan, Gokula Vasantha, et al. Hierarchical ensemble deep learning for data-driven lead time prediction, The International Journal of Advanced Manufacturing Technology, 2023.
Ayse Aslan, Hanane El-Raoui et al. Using worker position data for human-driven decision support in labour-intensive manufacturing, Sensors, 2023.