Milto Miltiadou
2 months ago
This scholarship has expired. You can find similar scholarships from the section below or browse our scholarships listing pages.
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
Aug 17, 2026
Country
United Kingdom
University
University of Exeter

How do I apply for this?
Sign in for free to reveal details, requirements, and source links.
Meet Kite AI
Ongoing programUniversity of Exeter PhD Studentship
by University of Exeter
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.
Explore the programSuggested scholarships
Keywords
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.
Ask ApplyKite AI
Professors

How do I apply for this?
Sign in for free to reveal details, requirements, and source links.