Fully-Funded PhD in Low-Latency AI/ML-aided Data Pipeline Architecture for Digital Twin as a Service (DTaaS)

The Broadband Communication Research Group (BCRG), operating under Edinburgh Napier University's School of Computing, Engineering & The Built Environment, specializes in cutting-edge research in digital twin systems, AI-enabled autonomous networks, and real-time communication. With an emphasis on innovation, BCRG drives advancements in next-generation edge computing, IoT technologies, and 6G networks. Our team is committed to advancing research that shapes the future of communication systems and supports a collaborative research environment (www.bcrg.uk)
Role DescriptionThe growth of Digital Twin (DT) has been moving towards the Digital Twin as a Service (DTaaS) approach, which provides the components within DT to be developed and deployed at independent services. This enables scalability and flexibility through supporting distributed deployments across geographically dispersed and heterogeneous infrastructures.
However, this architectural shift introduces significant challenges. The modular architecture requires continuous data exchange between different microservices, which may be deployed across wide-area networks. Meanwhile, the extensive data-driven modelling and Artificial Intelligence (AI) and Machine Learning (ML) inference at DTs bring computational challenges. Here, the size, execution time, and resource consumption of models threaten the timeliness of DTs. Furthermore, as more sensitive and detailed data are being integrated, the cybersecurity attack surface expands and the challenging nature of ensuring security further increases with the low-latency requirements of DTs. Therefore, low-latency and scalable pipelines that integrate AI/ML are needed. Considering these, this project will focus on ML- assisted data pipelines. Here, the researcher will explore and analyze the communication and computation performance of AI/ML models and their effects on the DT performance. Correspondingly, the PhD student will work on designing lightweight and secure data transfer protocols.Academic qualificationsA first degree (at least a 2.1) ideally in Computing or Cybersecurity with a good fundamental knowledge of Python.
English language requirementIELTS score must be at least 6.5 (with not less than 6.0 in each of the four components). Other, equivalent qualifications will be accepted. Full details of the University’s policy are available online.
Essential attributes:- Only a first-class honours degree, or a distinction at master level in a subject relevant to the PhD project will be considered, or equivalent achievements.- Experience of fundamental Computer Science- Competent in Algoritmic Design, Machine Learning.- Knowledge of Data Management, and Digital Twins- Good written and oral communication skills- Strong motivation, with evidence of independent research skills relevant to the project- Good time management.
Desirable attributes:- Practical experience in research or industry will be considered an advantage.When applying, please quote the application reference “SCEBE1125” on your form.
APPLICATION CHECKLIST- Completed application form- CV- 2 academic references, using the Postgraduate Educational Reference Form (download)- Research project outline of 2 pages (list of references excluded). The outline may provide details about--- Background and motivation of the project. The motivation, explaining the importance of the project, should be supported also by relevant literature. You can also discuss the applications you expect for the project results.--- Research questions or objectives.--- Methodology: types of data to be used, approach to data collection, and data analysis methods.--- List of references.
The outline must be created solely by the applicant. Supervisors can only offer general discussions about the project idea without providing any additional support.- Statement no longer than 1 page describing your motivations and fit with the project.- Evidence of proficiency in English (if appropriate)
To be considered, the application must use- “SCEBE1125” as project code.- the advertised title as project title
Studentship Start Date: October 2026
For informal enquiries about this PhD project, please contact b.canberk@napier.ac.ukApplication Enquiries: Application GuidanceApplication link: https://evision.napier.ac.uk/si/sits.urd/run/siw_sso.go?0vc7d1WTl3NzdfMu4Kdt6FihZAAfYBYO93kSfkA57FuJ1JSzS3
Funding NotesThe studentships will cover full UK or international tuition fees and will include a standard living allowance at the RCUK rate (currently £21,383 per annum, normally increased annually). International applicants should note that visa application costs and the NHS health surcharge are not included in the studentship, and successful applicants will need to cover these expenses themselves.
References:A. Masaracchia, V. Sharma, B. Canberk, O. A. Dobre and T. Q. Duong, "Digital Twin for 6G: Taxonomy, Research Challenges, and the Road Ahead," in IEEE Open Journal of the Communications Society, vol. 3, pp. 2137-2150, 2022, doi: 10.1109/OJCOMS.2022.3219015.C. Alcaraz and J. Lopez, "Digital Twin: A Comprehensive Survey of Security Threats," in IEEE Communications Surveys & Tutorials, vol. 24, no. 3, pp. 1475-1503, thirdquarter 2022, doi: 10.1109/COMST.2022.3171465.L. V. Cakir, M. Özdem, H. Ahmadi, T. Q. Duong and B. Canberk, "Internet of Twins Approach: Digital-Twin-as-a-Platform Architecture," in IEEE Internet Computing, vol. 29, no. 1, pp. 65-74, Jan.-Feb. 2025, doi: 10.1109/MIC.2024.3491915.