AI-Driven Digital Twin for Predictive Maintenance in Smart Manufacturing

The rapid adoption of Industry 5.0 technologies is transforming manufacturing through intelligent automation, sustainability, and human-centric production systems. Digital Twins—virtual representations of physical assets and processes—are emerging as a key technology for improving operational efficiency, reducing downtime, and optimizing energy consumption in smart factories.

This PhD project aims to develop an AI-enhanced Digital Twin framework for predictive maintenance and energy optimization in industrial environments. The research will investigate the integration of Industrial Internet of Things (IIoT) sensors, real-time data analytics, machine learning algorithms, and Digital Twin technologies to monitor equipment health, predict failures before they occur, and recommend optimal maintenance actions. In parallel, the project will explore AI-driven strategies for reducing energy consumption while maintaining productivity and system reliability.

The research will involve the design and implementation of a scalable Digital Twin architecture, data acquisition from industrial sensors, development of predictive models, and validation through simulation and experimental case studies. The proposed framework will support real-time decision-making and contribute to the development of more sustainable, resilient, and efficient manufacturing systems.

This project aligns strongly with current European priorities in Industry 5.0, smart manufacturing, digitalisation, and sustainability. The outcomes are expected to be of significant interest to manufacturing, pharmaceutical, food processing, and automation industries.

The successful candidate will gain expertise in Digital Twins, Artificial Intelligence, Industrial IoT, data analytics, and smart manufacturing technologies. The project is expected to generate multiple high-quality journal and conference publications and offers opportunities for collaboration with industrial partners and international research networks.

Applicants should hold a minimum of a Bachelor's degree or a Master's degree in Electronic Engineering, Electrical Engineering, Automation Engineering, Mechatronics, Computer Engineering, Computer Science, Data Science, Manufacturing Engineering, or a closely related discipline.

The ideal candidate will have an interest in one or more of the following areas:

Artificial Intelligence and Machine Learning
Industrial Automation and Control Systems
Industrial Internet of Things (IIoT)
Digital Twin Technologies
Data Analytics and Predictive Modelling
Smart Manufacturing and Industry 5.0
Sensor Systems and Data Acquisition
Programming and Software Development

Experience with programming languages such as Python, MATLAB, C/C++, or similar tools is desirable but not essential. Knowledge of machine learning frameworks, cloud computing platforms, simulation environments, or industrial communication protocols would be advantageous.

The successful candidate should demonstrate:

Strong analytical and problem-solving skills.
The ability to work independently and as part of a multidisciplinary research team.
Good written and verbal communication skills in English.
A strong motivation to undertake innovative research and publish in high-quality international journals and conferences.

This project is particularly suitable for candidates seeking to develop expertise in Artificial Intelligence, Digital Twins, and Smart Manufacturing technologies, which are among the most sought-after skills in modern industry and research.

Self Funded (Scholarship not available. Fees & Materials to be paid by the student)

If you are interested in submitting an application for this project, please complete an Expression of Interest.

 https://forms.office.com/e/0hCcrv2Gkp