About the Project
Supervisory team: Dr Rafael Mestre and Professor Mohammad Belal
Use fibre-optic cables as large-scale sensors across cities, coasts, and oceans. This PhD will develop machine-learning and signal-processing methods for state-of-the-art distributed acoustic sensing datasets. You will analyse complex spatiotemporal signals, combine them with environmental and operational data, and investigate applications in infrastructure, transport, environmental monitoring, and marine sensing.
Distributed Acoustic Sensing (DAS) turns optical fibres into a dense array of vibration sensors. It could transform how we monitor infrastructure, transport, environmental processes, human activity, and marine environments. However, DAS produces large, noisy datasets that are difficult to label, creating challenges for signal processing and machine learning.
This PhD will develop computational methods for analysing DAS data across urban and maritime settings. You will have access to state-of-the-art datasets collected across the south of England and London, alongside maritime datasets. DAS measurements can be combined with secondary datasets, including vehicle counts, air-pollution records, and local-authority information to contextualise and validate the signals detected by the fibre. These datasets create opportunities for data fusion, cross-environment comparison, and rigorous validation.
You will shape the technical contribution. Possible directions include self-supervised or weakly supervised learning; event detection, classification, localisation, and source separation; representation learning for spatiotemporal signals; uncertainty and interpretability; domain adaptation across cables, locations, and environments; efficient high-volume processing; and validation using incomplete ground truth. Applications could include transport, infrastructure, environmental hazards, ocean acoustics, marine activity, or ecological monitoring.
The project builds on capabilities and datasets developed through SOUNDSCALE project. You will work across computer science, engineering, signal processing, environmental research, and responsible innovation, with access to high-performance computing and collaboration with the National Oceanography Centre (NOC) and researchers working across urban and marine sensing applications.
You will join the University of Southampton’s School of Electronics and Computer Science, ranked 12th in the UK for Computer Science and 4th for Electrical and Electronic Engineering in the Complete University Guide 2027. The ECS Graduate School provides training in research methodology and transferable skills, supplemented by professional-development opportunities through the Doctoral College.
You will have access to Iridis, the University’s high-performance computing facility, and opportunities to work with researchers and facilities at the National Oceanography Centre.
Further opportunities may include teaching experience, conference participation, engagement with the Alan Turing Institute and Responsible AI UK, and support from Future Worlds to explore the commercial potential of your research.
The School of Electronics & Computer Science is committed to promoting equality, diversity inclusivity as demonstrated by our Athena SWAN award. We welcome all applicants regardless of their gender, ethnicity, disability, sexual orientation or age, and will give full consideration to applicants seeking flexible working patterns and those who have taken a career break. The University has a generous maternity policy, onsite childcare facilities, and offers a range of benefits to help ensure employees’ well-being and work-life balance. The University of Southampton is committed to sustainability and has been awarded the Platinum EcoAward.
Entry requirements
You must have a UK 2:1 honours degree, or its international equivalent in one of the following:
- computer science
- electrical or electronic engineering
- data science
- physics
- mathematics
- acoustics
- or another relevant discipline
Essential skills:
- programming and quantitative analysis, especially in Python or MATLAB
- machine learning
Desirable skills (applicants are not expected to have all of these skills):
- experience with deep learning
- digital signal processing
- time-series analysis
- high-performance computing
- large scientific datasets
Prior knowledge of distributed acoustic sensing or fibre-optic sensing is not required.
Due to funding limits, only UK and EU-based candidates are invited to apply.
Fees and funding
Due to funding limits, only UK and EU-based candidates are invited to apply.
Horizon Europe fee waivers automatically cover the difference between overseas and UK fees for qualifying students.
Competition-based Presidential Bursaries from the University cover the difference between overseas and UK fees for top-ranked applicants.
Competition-based studentships offered by our schools typically cover UK-level tuition fees and a stipend for living costs for top-ranked applicants.
Funding will be awarded on a rolling basis, so apply early for the best opportunity to be considered.
For more information, please visit our postgraduate research funding pages.
How to apply
You need to:
- choose programme type (Research), 2027/28, Faculty of Engineering and Physical Sciences
- select Full time or Part time
- search for programme PhD Computer Science (7089)
- add name of the supervisor in section 2 of the application
Applications should include:
- your CV (resumé)
- 2 academic references
- degree transcripts and certificates to date
- English language qualification (if applicable)
Contact us
Faculty of Engineering and Physical Sciences
If you have a general question, doctoralcollege-admissions@soton.ac.uk.
Project leader
For an initial conversation, r.mestre@soton.ac.uk