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Alessandro Leonardi

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PhD in Physics-Based Machine Learning for Forest Mitigation of Landslide Runout University of Sheffield in United Kingdom

Degree Level

PhD

Field of study

Computer Science

Funding

Fully funded for 3.5 years, including full tuition fees, a tax-free UKRI-rate maintenance stipend currently £21,805 per year, and a research grant. Funding is awarded competitively, with restrictions on the number of international studentships.

Deadline

Jan 13, 2027

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Country

United Kingdom

University

University of Sheffield

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Keywords

Computer Science
Environmental Science
Civil Engineering
Geotechnical Engineering
Earth Science
Climate Resilience
Natural Hazard
ML

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About this position

The University of Sheffield is offering a fully funded PhD investigating how forests can mitigate landslide runout and reduce risks to infrastructure. The project is supervised by Alessandro Leonardi and Dr Jill Johnson, in collaboration with Adrian Koe at AtkinsRéalis, and is supported through the EPSRC Doctoral Landscape Award.

The research will combine physics-based numerical simulations, machine learning and laboratory experiments to understand how forest structure and management affect the movement of landslide debris. The successful researcher will model interactions between landslides and trees, generate synthetic datasets covering varied forest configurations and landslide conditions, and develop predictive tools for rapidly estimating runout and evaluating uncertainty.

Experimental work using laboratory flume experiments will be used to validate numerical predictions and improve understanding of landslide–vegetation interactions. Through the industry collaboration, the project will also examine how forest management, thinning and reforestation may influence landslide risks to roads, railways and buildings.

The overall aim is to create an automated assessment tool for engineers and land managers making decisions about woodland management, infrastructure resilience and climate adaptation. The interdisciplinary research environment connects computational modelling, artificial intelligence, experimental geotechnical engineering, natural hazard assessment and nature-based solutions.

Funding details

Fully funded for 3.5 years, including full tuition fees, a tax-free UKRI-rate maintenance stipend currently £21,805 per year, and a research grant. Funding is awarded competitively, with restrictions on the number of international studentships.

How to apply

Use the project and application information link provided in the first comment of the LinkedIn post. Submit the application by 13 January 2027 at 17:00 GMT.

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University Name
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United Kingdom

Research Interests

Geotechnical Engineering

Earth Science

Natural Hazard

Climate Resilience

Environmental Science