PhD Studentship: CENTA 2027-B17 Finding the most explosive volcanoes on earth

PROJECT HIGHLIGHTS

• Identify hidden high-risk volcanoes using global datasets, statistical analysis, and machine learning to predict systems with greatest potential for generating large-magnitude eruptions, and thereby determine knowledge gaps.
• Conduct targeted field investigations at identified priority volcanoes in Southeast Asia, collecting eruption records, geological samples, and hazard data to test model predictions, with opportunities to share methods through an international joint field course.
• Produce actionable hazard insights to guide monitoring priorities and inform catastrophe risk models, supporting disaster risk reduction policies and strengthening systemic resilience to globally significant volcanic events.

Overview

Finding the Most Explosive Volcanoes on Earth

While advances in volcanology have revealed much about eruption processes, research has largely concentrated on a small set of well-monitored volcanoes, geographically skewed towards continental and northern hemisphere regions. This focus has left significant gaps in our understanding of many volcanoes, especially in resource-poor regions including island arcs in Southeast Asia. These areas coincide with the world’s highest volcanic activity, yet eruption histories, geological maps, and monitoring data are incomplete or absent. Volcanoes capable of very large magnitude eruptions (M ≥ 6), with the potential to cause severe regional or global impacts, remain missing from current risk assessments.

This PhD project will address this critical knowledge gap by identifying which volcanoes are most likely to produce highly explosive, large-magnitude eruptions. The student will combine global datasets with targeted field investigations to test the attributes hypothesised to favour such eruptions. Using a training dataset of well-studied volcanoes with known large eruptions, the project will employ statistical and machine learning (ML) methods to identify the strongest predictors of eruption magnitude. These predictive models will then be applied to poorly characterised volcanoes, producing a prioritised list for further research and monitoring.

Fieldwork in Southeast Asia, in collaboration with Indonesian partners, will test and refine these predictions. The student will also help organise a joint field course (with the Global Volcano Risk Alliance charity), to train local and international students in volcanic mapping, tephra sampling, dating techniques, and the analysis of volcanic ash for health impacts. This component will strengthen scientific capacity, foster community engagement, and produce valuable new data for the project.

By integrating advanced data science with traditional field volcanology and community-based training, the project will identify hidden high-risk volcanoes and contribute directly to monitoring priorities and the next generation of volcano scientists. The outcomes will be relevant to global disaster risk reduction efforts and will support targeted investment in monitoring infrastructure where it is most urgently needed.

Applicants should be comfortable working with quantitative analytical methods (specific machine learning skills can be developed during the PhD). Willingness for fieldwork in challenging environments is essential.

Figure 1: The lake-filled caldera of Rinjani volcano in Lombok, Indonesia, the site of the Samalas eruption in 1257 that had global climatic effects. The volcano culprit for these climatic effects was only discovered to be Rinjani as recently as 2013.

Case Projects

This project does not offer a CASE studentship

Host

• University of Birmingham

Theme

• Dynamic Earth

Supervisors

Project investigator

• Michael Cassidy m.cassidy.1@bham.ac.uk

Co-investigators

• Gregor Weber g.weber@soton.ac.uk; Sebastian Watt (Geography, Earth and Environmental Sciences) s.watt@bham.ac.uk Annie Winson – BGS anwin@bgs.ac.uk

How to apply

Each host has a slightly different application process.

Find out how to apply for this studentship.

All applications must include the CENTA application form.

Choose your application route

Methodology

The project begins with compiling a global database of volcano attributes, integrating geological, geochemical, geophysical, and historical eruption data from multiple open and proprietary sources. A subset of well-characterised volcanoes with documented large eruptions (M ≥ 6–7) will serve as a supervised training set for statistical and ML models, enabling identification of attributes most predictive of extreme explosivity. These models will be applied to under-studied volcanoes to generate a ranked list of candidates for further investigation.

Fieldwork will target selected volcanoes, focusing on geological mapping, tephra sampling, and dating of eruptive deposits. The student will co-lead a joint field course with Indonesian partners and a UK charity, integrating training for students with data collection for the project. Laboratory analyses of tephra, including health impact assessment, will be undertaken in collaboration with partner institutions. Model outputs and field data will be iteratively integrated to refine predictions and produce actionable monitoring recommendations.

Further details and How to Apply

For any enquiries related to this project please contact Michael Cassidy m.cassidy.1@bham.ac.uk.

To apply to this project:

• You must include a CENTA studentship application form, downloadable from: CENTA Studentship Application Form 2027
• You must include a CV with the names of at least two referees (preferably three) who can comment on your academic abilities.
• Please submit your application and complete the host institution application process via: https://admissions.bham.ac.uk/course-finder-landing-page/?code=LES068 Please select the PhD in Geography (CENTA) 2027 entry year Apply Now button. The CENTA Studentship Application Form 2027 and CV can both be uploaded to the Personal Statement section of the online form.  In the funding section of the online form please select Research Council Funding and then choose Natural Environment Research Council (NERC). Please quote CENTA 2027-B17 when completing the application form.

Applications must be submitted by 23:59 GMT on Wednesday 6th January 2027.