PhD Scholarship in Computer Science / Machine Learning with Earth Observation for forest related applications - PhD (Funded) Ref: 5898

Supervisors

Milto Miltiadou - University of Exeter - Faculty of Environment, Science and Economy

The University of Exeter's Department of Computer Science invites applications for a funded PhD studentship. The successful candidate is expected to commence between 1 November 2026 and 1 April 2027 (subject to signing the partners' agreement before the start date). For eligible students the studentship will cover Home or International tuition fees plus an annual tax-free stipend of at least £ 21,805 for 3.5 years full-time.  The student would be based in Computer Science in the Faculty of Environment, Science and Economy at the Streatham Campus in Exeter.

This is a 3.5 year PhD studentship funded by the ADA project, University of Exeter.

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:

1. “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

2. “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

3. “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.   

International applicants need to be aware that they will have to cover the cost of their student visa, healthcare surcharge and other costs of moving to the UK to do a PhD.

Entry requirements

Applicants for this studentship must have obtained, or be about to obtain, a First or Upper Second Class UK Honours degree, or the equivalent qualifications gained outside the UK, in an appropriate area of Computer Science, Data Analysis or Mathematics. Applicants with degrees in Geography, Remote Sensing, or Forest ecology may be considered if adequate technical background is demonstrated.

If English is not your first language you will need to meet the English language requirements and provide proof of proficiency. Click here for more information.

How to apply

Apply now

To apply, please click the ‘Apply Now’ button above. In the application process you will be asked to upload several documents:

• CV
• Cover Letter
• Research proposal (max 300 words) explaining how innovative algorithms can solve one application areas (listed or relevant).
• Transcript(s) giving full details of subjects studied and grades/marks obtained (this should be an interim transcript if you are still studying)
• Two references from referees familiar with your academic work. If your referees prefer, they can email the reference direct to PGRApplicants@exeter.ac.uk quoting the studentship reference number (5898). References must be submitted with the application by August 17th 2026.
• If you are not a national of a majority English-speaking country you will need to submit evidence of your proficiency in English.

The closing date for applications is midnight on 17th August 2026. 

Interviews will be held virtually in the weeks commencing August 24^th or 31^st.

All application documents must be submitted in English. Certified translated copies of academic qualifications must also be provided.

Please quote reference 5898 on your application and in any correspondence about this studentship.

Summary