Director of Studies: Professor Sarah Boulton
Second Supervisor: Dr Martin Stokes
Third Supervisor: Dr Suryodoy Ghoshal
CASE Primary supervisor: Dr Michael Whitworth

Applications are invited for a 3.5 years PhD studentship. The studentship will start on 01 October 2027.

Shallow landslides (i.e., slope failures in surface material typically < 2 m thick) are a global hazard, posing significant risk to infrastructure and populations (1) especially in seismically active regions. However, it is still not possible effectively predict the location of future landslides (2, 3). A significant factor in the occurrence of shallow landslides is the amount of material available to fail; the mobile soil layer above solid bedrock called regolith. Regolith controls the size and distribution of shallow landslides across unstable landscapes. Yet, global knowledge of the thickness and physical properties of this horizon is extremely rare.  Recent modelling (3) using a novel landscape evolution model component and a co-seismic landslide inventory suggests a different approach - that regional regolith thickness can instead be inferred from databases of landslide occurrences using inverse modelling.  

To unlock the potential of this novel approach, this PhD will utilise the dynamic landscape of the central Italian Apennines, an accessible and data rich area prone to landslides and earthquakes (5), to test if the modelled regolith thicknesses correlate with field derived observations. This project addresses a fundamental knowledge gap, providing vital information to improve hazard modelling and mitigation strategies in vulnerable regions worldwide.

The researcher will: 1) undertake ensemble modelling of shallow landslides using the ShallowLandslider (3) Landlab component to generate and compare plausible subsurface regolith configurations; 2) assess spatial patterns of plausible landslides against a regional multi-temporal dataset to infer the best fit regolith characteristics; 3) undertake field data collection and follow-up laboratory analysis to constrain the regolith parameters and soil production rates across a number of key locations in the Central Apennines, and 4) test model outputs against field-constraints to improve landslide forecasting.

The individual joins a team of international experts who will support through training in computer modelling and programming (Python/Matlab), remote sensing and GIS, field geomorphic techniques (geomorphic mapping, TruPulse, DGPS/drone surveys), geochemistry, engineering geology methodologies (slope stability, rock strength assessment) and transferable research skills.  

We seek a candidate confident in undertaking fieldwork and data integration across geological disciplines. Prior experience of GIS/Remote sensing or programming is desirable. 

1) Froude, M.J. and Petley, D.N., 2018. Global fatal landslide occurrence from 2004 to 2016. Natural hazards and earth system sciences, 18(8), pp.2161-2181.

2) Jones, J. N., Boulton, S. J., Bennett, G. L., Stokes, M., & Whitworth, M. R. Z., 2021. Temporal Variations in Landslide Distributions Following Extreme Events: Implications for Landslide Susceptibility Modeling. Journal of Geophysical Research: Earth Surface, 126(7). doi:10.1029/2021jf006067

3) Jones, J.N., Boulton, S.J., Stokes, M., Bennett, G.L. and Whitworth, M.R., 2021. 30-year record of Himalaya mass-wasting reveals landscape perturbations by extreme events. Nature communications, 12(1), p.6701.

4) Ghoshal, S., Boulton, S.J., Hales, T.C., Bennett, G., Beswick, A., Jones, J.N., Lewin, S., Mildon, Z.K., Stokes, M., Whitworth, M.R. and Campforts, B., 2025. ShallowLandslider: a physics-based component for predicting regional distributions of coseismic landslides. EarthArXiv.

5) Atkinson, P.M. and Massari, R., 1998. Generalised linear modelling of susceptibility to landsliding in the central Apennines, Italy. Computers & Geosciences, 24(4), pp.373-385.

Acceptable first degree subject(s): A degree in a geoscience discipline (e.g., geology, Earth sciences, physical geography, geophysics).

If your first language is not English, you will need to meet the minimum English requirements for the programme, IELTS Academic score of 6.5 (with no less than 5.5 in each component test area) or equivalent. 

ARIES studentships are subject to UKRI terms and conditions. Successful candidates who meet UKRI’s eligibility criteria will be awarded a fully-funded studentship, which covers fees, maintenance stipend £21,805 p.a. for 2026/27 (2027/28 TBC) and a research training and support grant (RTSG). 

A limited number of studentships are available for international applicants, with the difference between 'home' and 'international' fees being waived by the registering university. 

Please note, however, that ARIES funding does not cover additional costs associated with relocation to, and living in, the UK, such as visa costs or the health surcharge.

NB: The studentship is supported for 3.5 years of the four-year registration period. The subsequent 6 months of registration is a self-funded ‘writing-up’ period.

Working part-time while receiving a stipend at the University of Plymouth is generally permitted, provided it does not conflict with the terms of your funding or visa requirements. 

If you wish to discuss this project further informally, please contact Professor Sarah Boulton.

Please view a list of supporting documents to upload with your application.

For more information on the admissions process generally, please visit our how to apply for a research degree webpage or contact the Doctoral College at research.degree.admissions@plymouth.ac.uk

Shortlisted candidates will be invited for interview after the deadline. We regret that we may not be able to respond to all applications. Applicants who have not received a response within six weeks of the closing date should consider their application has been unsuccessful on this occasion.