This PhD project at the University of Leeds offers an exciting opportunity to explore the Earth's deep interior using cutting-edge light-based seismic sensors, specifically distributed acoustic sensing (DAS) technology. The Earth's mantle is a dynamic region, with processes such as subducting slabs, mantle plumes, and convection driving plate tectonics, earthquakes, and volcanism. Despite advances in understanding broad-scale mantle dynamics, many details remain uncertain, and this project aims to address these mysteries by leveraging fibre-optic sensors to 'see' into the solid Earth.

Distributed acoustic sensing (DAS) uses optical fibres to measure strain and interpret seismic waves, providing a novel approach to subsurface monitoring. While DAS has been widely used for local and regional applications, its potential for global seismology and regional monitoring is only beginning to be realized. The project will utilize extensive DAS datasets collected during the Global DAS Month, including recordings from the Eskdalemuir seismic observatory in Scotland and the NORFOX network in Norway. These datasets include seismic events such as the M7.8 Kahramanmaraş earthquakes in Turkey and Syria, offering a rich testing ground for comparing DAS data with conventional seismometer arrays and seismic nodes.

As a PhD student, you will develop new skills and methods for detecting teleseismic earthquake arrivals, with a particular emphasis on machine learning techniques for data denoising and automated processing. The project structure is flexible and can be tailored to your research interests, with possible directions including optimizing processing parameters for DAS data, applying array processing methods to classify seismic arrivals, comparing measured propagation angles with global Earth models, and characterizing ground properties using DAS and co-located arrays. There is also scope to identify new sites for DAS deployment and gather novel datasets.

This research is highly collaborative, involving leading experts in seismology, global geophysics, and DAS technology from Leeds, AWE Blacknest, and NORSAR. The work has significant impact potential, both in advancing fundamental understanding of Earth's structure and in practical applications such as monitoring adherence to the Comprehensive Nuclear-Test-Ban Treaty (CTBT). Successful candidates will be well-positioned to publish in high-impact journals and present at international conferences.

Funding is provided through the YES-DTN program, which offers fully funded studentships covering university fees, a personal stipend, and research and training costs. The program is open to UK and international applicants, though the number of awards for international students is limited by UKRI rules. International applicants must cover visa and health surcharge costs.

Applicants should have a strong interest in Earth science, geophysics, or seismology, and be motivated to work with large datasets using computational and machine learning techniques. A background in data science, applied geology, or geoscience is desirable. The application deadline is January 7, 2026. For more information and to apply, visit the project page and the YES-DTN website.