At a Glance
- Tasks: Conduct innovative research using machine learning and Earth observation data for forest applications.
- Company: University of Exeter, a leading institution in environmental science and technology.
- Benefits: Fully funded tuition fees and a tax-free stipend of at least £21,805 per year.
- Other info: Collaborate with top researchers and gain valuable experience in a dynamic academic environment.
- Why this job: Make a real impact on global forest conservation through cutting-edge research.
- Qualifications: Strong background in computer science or related fields; passion for machine learning.
PhD Scholarship in Computer Science / Machine Learning with Earth Observation for forest related applications - PhD (Funded) University of Exeter - ESE
Qualification Type: PhD
Location: Exeter
Funding for: UK Students, EU Students, International Students, Self-funded Students
Funding amount: UK and International tuition fees and an annual tax-free stipend of at least £21,805 per year
Hours: Full Time
Placed On: 10th July 2026
Closes: 24th August 2026
Reference: 5898
The Continuous Release Of Earth Observation (satellite) Data And The Emergence Of Machine Learning Methods Open Up New Possibilities For Understanding Forests. These Large Datasets Provide Complementary Information On 3D Structure (GEDI Lidar, BIOMASS P-band Radar, NISAR L-band Radar) And High Spatiotemporal Resolution (Sentinel-1 C-band Radar, Sentinel-2 Multispectral).
State-of-the-art Foundation Models (e.g., AlphaEarth, TerraMind) Are Currently Being Evaluated For Different Applications, But The Inclusion Of Temporal Components And Newly Available Datasets In Foundation Models Remains Limited. There Is Also a Need For Accounting Noise In Deep Learning Models And Quantifying Uncertainty In Real-world Applications.
This PhD Studentship (scholarship) Leverages Large-scale Earth Observation Data To Evaluate And Advance Machine Learning Algorithms For One Of The Following Application Areas:
- “Characterising forests variations in relation to distance from pre-Columbian earthworks in the Amazon forest, Brazil”, co-supervisor Prof Ted Feldpausch, University of Exeter, UK
- “Predicting mixed-forest composition and/or understanding intra-variability of same forest types at European level”, co-supervisor Dr Emily Lines, University of Cambridge, UK
- “Quantifying forest planation damage and supporting planning after a cyclone or tropical storm in New Zealand”, in collaboration with Interpine Group Ltd, NZ
The prospect candidate is requested to choose one application (listed or relevant) and write a 300 word proposal on how innovative algorithms can tackle it. Applicants are encouraged to reach out to the lead supervisor, Dr Milto Miltiadou (m.miltiadou@exeter.ac.uk), to gain insight into the specialised data available and the associated challenges of each proposed project. The studentship will be awarded based on merit. Both Home and International Students are eligible.
The PhD funding includes tuition fee coverage and stipend.
PhD Studentship: PhD Opportunity TitlePhD Scholarship in Computer Science / Machine Learning wi[...] in Exeter employer: Emerging Scholars Council
Newcastle University is an exceptional employer, offering a vibrant academic environment that fosters innovation and collaboration. With a strong commitment to research excellence and professional development, employees benefit from access to cutting-edge resources and opportunities for growth in the field of implementation science. Located in a dynamic city known for its rich cultural heritage and supportive community, Newcastle University provides a unique setting for meaningful and impactful work.
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