Validation and Testing of Data-Driven Digital Twins for Production Systems
The project’s main objective is to develop a methodology for continuous validation of Digital Twins (DTs) for production systems. Validation is an integral part and an enabler of Digital Twin development as DTs need to reflect the current state of the real system in near real-time. This project is part of ONE4ALL (Horizon Europe 2022) aiming to boost manufacturing plants’ transformation towards Industry 5.0.
PhD student: Ashkan Zare
Supervisor: Prof. Sanja Lazarova-Molnar
Project period: August 2023 – July 2026
Software Engineering of Machine-Learning-based Analytics for IoT Data
The goal of this doctoral project is to design and develop scalable systems for machine-learning-based analytics of IoT data. This project will incorporate developing software technologies based on novel software architectures and semantic technology to enable scalable machine-learning based analytics of IoT data. The technologies will be developed and evaluated for the analysis of IoT data for buildings.
PhD student: Henrik Dyrberg Egemose.
Supervisor: Mikkel Baun Kjærgaard