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source

Milto Miltiadou

3 months ago

University of Exeter PhD Scholarship

Fully Funded PhD in Computer Science / Machine Learning with Earth Observation for Forest Applications University of Exeter in United Kingdom

Degree Level

PhD

Field of study

Computer Science

Funding

Fully funded PhD studentship for 3.5 years. Covers Home or International tuition fees plus an annual tax-free stipend of at least £21,805. International applicants must cover their own visa, healthcare surcharge, and relocation costs.

Deadline

Expired

Country flag

Country

United Kingdom

University

University of Exeter

Ongoing program

University of Exeter PhD Studentship

by University of Exeter

Varies
PhD
3.5 Years Full-time
United Kingdom

University of Exeter PhD Studentships support doctoral research at Exeter. Funding varies by project; some are fully funded for Home applicants, while international applicants may receive only partial fee support.

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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 l...

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Keywords

Computer Science
Machine Learning
Environmental Science
Biology
Remote Sensing
Geography
Earth Science
Uncertainty Analysis
Earth Observation
Rainforest Ecology
Statistics

About this position

University of Exeter is advertising a fully funded PhD in Computer Science / Machine Learning with Earth Observation for forest-related applications. The project sits in the Department of Computer Science, Faculty of Environment, Science and Economy, and is linked to DASOS’ Vision Group and the Centre for Environmental Intelligence.

The research focuses on advancing algorithms for large-scale Earth Observation and LiDAR data to improve forest monitoring. The project highlights methodological work in machine learning, including feature engineering, representation learning, classification, regression, anomaly detection, change analysis, and uncertainty quantification. It also mentions the use of multi-sensor datasets such as Sentinel-1, Sentinel-2, GEDI lidar, BIOMASS, and NISAR, with interest in foundation models and temporal modelling.

Applicants must choose one application area and write a 300-word proposal on how innovative algorithms can address it. The listed themes are: characterising forest variation near pre-Columbian earthworks in the Amazon, predicting mixed-forest composition and intra-variability in Europe, or quantifying forest plantation damage and supporting recovery after cyclones or tropical storms in New Zealand.

Eligibility: applicants should have, or be about to obtain, a First or Upper Second Class UK Honours degree or equivalent in Computer Science, Data Analysis, or Mathematics. Candidates from Geography, Remote Sensing, or Forest Ecology may also be considered if they show strong computing skills. English language proficiency is required where applicable.

Funding: the studentship covers Home or International tuition fees and provides an annual tax-free stipend of at least £21,805 for 3.5 years. The post notes that international students must cover visa, healthcare surcharge, and relocation costs.

Deadline: 17 August 2026. Interviews are expected in the weeks commencing 24 or 31 August 2026. Applicants should apply via the University of Exeter portal, upload the required documents, and quote reference 5898.

Funding details

Fully funded PhD studentship for 3.5 years. Covers Home or International tuition fees plus an annual tax-free stipend of at least £21,805. International applicants must cover their own visa, healthcare surcharge, and relocation costs.

What's required

Applicants must have obtained, or be about to obtain, a First or Upper Second Class UK Honours degree or equivalent in Computer Science, Data Analysis, or Mathematics. Candidates with degrees in Geography, Remote Sensing, or Forest Ecology may also be considered if they demonstrate strong computing skills. Strong computing and machine learning skills are required. Applicants must submit a 300-word research proposal on one of the listed or a relevant alternative application. English language proficiency is required for non-native English speakers.

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Professors

Lecturer (E&R)

University Name
Country flag

United Kingdom

Research Interests

Climatology

Meteorology

Remote Sensing

Vegetation Dynamics

Rainforest Ecology

Prof at University of Exeter

University Name
Country flag

United Kingdom

Research Interests

Ecology

Physical Geography

Environmental Science

Remote Sensing

Machine Learning