Project Overview: This PhD opportunity at the University of Leeds focuses on imaging the Earth's deep interior using massive seismic datasets and advanced machine learning techniques. The project aims to unravel the complex processes of mantle convection, core-mantle interactions, and the evolution of continent-sized structures such as Large Low Velocity Provinces (LLVPs) and Ultra-Low Velocity Zones (ULVZs). These deep Earth features are crucial for understanding planetary evolution, surface processes, and the conditions that make Earth habitable. Research Focus: You will analyze global seismic data to map heterogeneity in the lowermost mantle, applying both supervised and unsupervised machine learning approaches. The project is structured in three flexible stages: (1) mapping small-scale heterogeneity along the core-mantle boundary using seismic scattering analysis, (2) developing machine learning methods to characterize waveform variations of core-reflected seismic phases, and (3) pursuing a subproject tailored to your research interests, leveraging insights and skills gained in the first two stages. Academic Environment: You will join the Deep Earth research group within the School of Earth & Environment, one of the largest global teams studying the Earth's deep interior. The group offers a collaborative and supportive environment, with daily interaction among supervisors, postdoctoral researchers, and fellow PhD students. International collaboration opportunities are available. Funding: This position is part of the YES-DTN program, which provides 25–26 fully funded studentships annually. Funding covers university fees, a personal stipend, and research/training costs. International applicants are welcome, but must cover visa and health surcharge costs, and awards for non-UK applicants are limited by UKRI rules. See application information for details. Eligibility: Suitable candidates will have a background in physics, geophysics, quantitative geology, environmental science, mathematics, or computer science. The project is ideal for those interested in applying modern data analysis and machine learning to geoscience problems. No specific GPA or language test requirements are listed, but international applicants must meet university entry and visa requirements. Application Process: Applications are open to UK and international candidates. The deadline is January 7, 2026. Prepare your application materials and submit via the YES-DTN website. For more details, visit YES-DTN application information or the FindAPhD project page . References: The project draws on recent advances in big data seismology and machine learning, with key references provided for further reading.

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.