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University of Exeter
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China Scholarship Council
Fully Funded PhD in Machine Learning, Earth Observation and Forest Classification at the University of Exeter University of Exeter in United Kingdom
Degree Level
PhD
Field of study
Computer Science
Funding
Fully funded PhD position. The post does not specify the stipend amount or detailed breakdown of tuition and other covered costs.
Country
United Kingdom
University
University of Exeter
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About this position
The University of Exeter is offering a fully funded PhD opportunity in Computer Science for a prospective student from China interested in machine learning, Earth observation and forests.
The project, “Improving confidence in continuous forest type classification maps when ground truth data are scarce,” will investigate computational approaches for producing more reliable forest classification maps in situations where labelled ground-truth data are limited. The research brings together machine learning, remote sensing, computer vision, Earth observation and GeoAI to address uncertainty and confidence in forest mapping.
The doctoral researcher will join the DASOS Vision Group, the Centre for Environmental Intelligence and the Department of Computer Science at the University of Exeter. The project is supervised by Lead Supervisor Milto Miltiadou and Second Supervisor Tinkle Chugh, providing a research environment spanning artificial intelligence, environmental data and forest-focused Earth observation.
This opportunity is intended for September 2027/28 entry and is specifically aimed at citizens or permanent residents of the People's Republic of China.
Funding details
Fully funded PhD position. The post does not specify the stipend amount or detailed breakdown of tuition and other covered costs.
What's required
Applicants must be citizens or permanent residents of the People's Republic of China and prospective PhD students with an interest in machine learning, Earth observation and forests. The opportunity is for September 2027/28 entry.
How to apply
Review the eligibility information at the provided link. Contact Lead Supervisor Milto Miltiadou by email to discuss the opportunity and application process.
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