Bridging the Gap: Designing Real-World Digital Twin Microcourses for Academia–Industry Impact, 23 July 2025, 10:00-12:00

This 2-hour hands-on workshop explores how Digital Twin (DT) technology can power the next generation of microcourses designed to bridge academic learning with real-world industry needs. Participants will learn from ongoing initiatives about how Digital Twins are transforming infrastructure and energy sectors.

The session includes:

• A guided talk highlighting the role of DTs in modular learning and industrial innovation.
• A collaborative brainstorming session, where participants co-design their own microcourse concepts around DT case studies.
• Group discussion on how microcourses can be used to foster academic–industry partnerships, especially for postdoctoral and early-career researchers.

This workshop draws from national programmes and real case studies, helping researchers shape agile, interdisciplinary training that responds to fast-changing industrial contexts.

Intended learning outcomes:

• Understand the role of Digital Twins in education, research, and infrastructure innovation.
• Identify key challenges and opportunities in designing microcourses that connect academic research to industry use-cases.
• Apply ideation techniques to co-create microcourse structures focused on Digital Twin technology.
• Co-design a prototype for a DT-based microcourse that could be delivered to either academic or industry stakeholders.
• Reflect on how modular, interdisciplinary learning (e.g., DT + AI + Infrastructure) can scale researcher impact and industry readiness.

Presenter Biography:

Dr. Shady Adib recently completed his PhD in Civil Engineering at Newcastle University, developing a hybrid Digital Twin model for real-time damage detection in infrastructure. By integrating physics-based and data-driven methods with IoT (Internet of Things), his work advances structural health monitoring through synchronising physical and virtual models to identify abnormal behaviour and address model uncertainties. Dr. Adib is currently exploring generative AI and physics-informed machine learning models to improve structural health monitoring accuracy through self-generating and hierarchical digital twins.