PhD Studentship: Digital Twins for waste reduction in Industry 5.0

Primary Supervisor: Dr. Mohsin Raza

Reducing industrial waste is a key challenge in achieving sustainable manufacturing and advancing the vision of Industry 5.0. Manufacturing processes can generate significant waste through material inefficiencies, defective products, equipment failures, excessive energy consumption, and suboptimal production planning. Digital twins, integrated with artificial intelligence (AI), the Internet of Things (IoT), and advanced data analytics, offer opportunities to monitor manufacturing operations, identify sources of waste, and support proactive, data-driven interventions.

This PhD project will investigate the development of intelligent digital twin frameworks to predict, monitor, and reduce waste in manufacturing processes. The research will explore how near real-time data from industrial sensors, machinery, and production systems can be integrated into digital twins to identify 2inefficiencies, detect anomalies and predict product defects, to optimise the use of raw materials and reduce waste. AI and machine learning techniques will be investigated to support waste prediction androot-cause analysis.

The project will explore simulation-based approaches that enable manufacturers to evaluate alternative production scenarios and assess their potential impact on waste generation before implementing changes in real-world environments. Relevant sustainability indicators may be incorporated to measure improvements in material utilisation and overall waste reduction. The research will further investigate key metrics to understand waste patterns and make informed operational decisions.

The successful candidate will contribute to innovative research addressing industrial waste and environmental sustainability. The project aims to develop intelligent digital twin solutions that help manufacturers minimise waste, improve resource efficiency, in line with Industry 5.0. The project will offer opportunities to develop innovative digital twin solutions, publish research in leading academic venues, and explore practical applications.

Entry Requirements

The standard minimum entry requirement is a 2:1 bachelor's degree.

Mode of Study

Full time

Start Date

1 October 2027

Additional Funding Information

This PhD project is in a competition for a Faculty of Science funded studentship. Funding is available to UK applicants and comprises ‘home’ tuition fees and an annual stipend for 3 years. The current UEA / institutional PhD stipend rate is £21,805 and will be subject to any agreed inflationary uplift for 2027/28.

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