PhD Studentship: UK Digital Ecological Landscapes

Start date: 01/10/2025    

Eligibility & Related Project Details

Fee status of eligible applicants: UK

Duration if full time preferred*: 3.5 years

Duration if part time preferred: 6 years

Supervisors: 1^st Supervisor: Dr Daniel Simms, 2^nd Supervisor: Dr Toby Waine

Sponsorship

Sponsored by NERC through CENTA DTP, Cranfield University. Successful home-fees-eligible candidates will receive an annual stipend, set at £19,237 per year (or pro rata), paid directly to the student in monthly increments, plus full university fees and a research training support grant (RTSG) of £8,000.

Please note: CENTA is currently awaiting confirmation of funding under the BBSRC-NERC Doctoral Landscape Award (DLA) scheme. This funding will support cohorts starting from 2025 onwards. We anticipate receiving further information by late October or early November 2024. The availability of funding, depends on this confirmation.

Intro Paragraph  

This is an exciting PhD opportunity in collaboration with the Peak District National Park, the first in the established in 1951. This research aims to automate the production of high-resolution habitat maps for diversity monitoring across UK landscapes for the first time. Accurate, scale dependant, information on habit location and condition is essential for understanding species distribution and movement, and to effectively target resources for nature recovery. This is urgent as there is a significant loss of biodiversity within the UK, with a 60% decline in the abundance of UK priority species since 1970.

Type of opportunity: Fully-funded studentship

Main Copy

More than a third of the Peak District national park (35%) is designated as Sites of Special Scientific Interest (SSSI) where important plants, wildlife and geological formations should be conserved. The project aligns with the Park’s efforts to enhance biodiversity monitoring and nature recovery across the UK's protected landscapes. Nationally, protected landscapes cover just over a quarter of the UK’s land area.

Recent advances in image-based AI allow us to evaluate the extent and distribution of habitats faster, more efficiently and with higher accuracy (van de Plas et al 2023, see figure 1). However, the variation in landcover caused by climate, seasonality and management requires a corpus of examples to train robust models. When combined with the limitations of image data collection (variation in timing, resolution, quality) and the semantics of habitat types, the lack of a high quality, representative datasets are a major limitation to the scaling of high-resolution predictions to the whole of the terrestrial UK (or globally).

Entry requirements

Applicants should have at least a 2:1 at UK BSc level or at least a pass at UK MSc level or equivalent in a related discipline.

Funding

The project is open to all applicants who meet the academic requirements (at least a 2:1 at UK BSc level or at least a pass at UK MSc level or equivalent). Please note the grant covers fee costs for a Home award. Unless you are eligible for such a Home award, you will need to consider how you will be able to meet any shortfall in funding for tuition fees, e.g. self-funded. Please contact the supervisors listed on the project for more information. 

How to apply  

For further information please contact:
Name: Dr Daniel Simms
Email: d.m.simms@cranfield.ac.uk
T: (0) 1234 750111

For information about applications please contact: study@cranfield.ac.uk 

Keywords: Ecological Landscapes, Remote Sensing, Data Science

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