PhD Studentship: PhD TitlePhD Scholarship in Computer Science​/Machine Learning wi

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., Alpha Earth, Terra Mind) 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:

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, , 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.

UK and International tuition fees and an annual tax‑free stipend of at least £21,805 per year