State date: 01.03.2025 Digital Twins (DTs) are becoming essential in the transport industry, enabling the replication of physical transport networks, vehicles, and logistics operations in a digital environment. These DTs provide real-time insights, predictive analytics, and operational optimisation across various modes of transport, including road, rail, air, and maritime. A multimodal transport network can generate massive amounts of data and information. To harness this data into DTs, without losses that can affect the accuracy of informed decisions, a robust communication technology infrastructure is essential. Furthermore, given the dynamic nature of DT technology, this infrastructure must be adaptable and agile to accommodate emerging advancements and evolving industry needs. Accordingly, ensuring high-fidelity, two-way synchronisation between the physical transport assets and their digital counterparts is crucial, but is a current knowledge gap/challenge, especially in large-scale and dynamic environments. The need for continuous, high-volume Internet-of-Things (IoT) connectivity across vast geographical areas adds complexity, potentially burdening communication infrastructure and leading to data lags. Maintaining real-time accuracy in DTs is crucial for decision-making, particularly in critical areas like traffic management, fleet optimisation, and safety monitoring. This PhD will aim to investigate the research question “How can we optimise heterogenous connectivity, ensuring high-fidelity synchronization, for digital twins to decarbonise multimodal transport networks?”. The project will work the following research objectives: Defining comprehensive strategies to harness the full potential of diverse data sources, through heterogeneous communication channels, for transport network optimisation. Optimising high-fidelity, two-way synchronisation for large-scale transport digital twins by developing strategies that ensure data accuracy and reliability across diverse and dynamic transport environments. Identifying and implementing the most effective IoT connectivity solutions that support high-volume, real-time data transfer in transport systems, minimising data lags and ensuring that the digital twin remains a precise and live reflection of its physical transport network or asset. As part of the EPSRC TransiT Hub, this PhD will work with people, from partner Universities, industry partners, and policy makers, and data from a range of different communities and places. Applicants with interests and expertise in systems engineering, digital technology, Cyber-Physical Systems/Infrastructure (CPS/I), Information and Communication Technology (ICT), Wireless Communication, and digital twins, would be highly relevant to this opportunity. The successful applicant will be affiliated with the Autonomous Systems and Connectivity Division and the Communication, Sensing, and Imaging Research Group. Before submitting a formal application, please notify Dr Ahmad Taha ([email protected]) of your interest by sending a one pager highlighting: Why are you interested in this project? Why are you the right candidate for the position? How this PhD will support you to achieve your future ambitions? Your email subject must be “TransiT PhD -  Optimising High-Fidelity Synchronisation and Internet-of-Things Connectivity for Large-Scale Transport Digital Twins ” How to Apply:  Please refer to the following website for details on how to apply: https://www.gla.ac.uk/postgraduate/research/autonomoussystems/