Research Assistant in Earth Observation and Machine Learning for Habitat Mapping

The University of Surrey invites applications for a Research Assistant (RA) to join the Space4Nature project. This exciting role focuses on utilising Earth Observation (EO) and citizen science data via Machine Learning (ML) techniques to map and assess UK Hab Classification (Level 3 and 4) habitats such as chalk grassland, heathland, acid grassland, etc. The project aims to develop scalable and replicable ML models for biodiversity conservation, addressing nature recovery and the urgent issue of habitat loss and fragmentation in Surrey County. The RA will be closely supervised and guided by Dr Ana Andries and Professor Stephen Morse of the Centre for Environment and Sustainability, University of Surrey.
Key responsibilities will include:
# Develop and apply EO and ML methodologies to classify and map habitats.
# Process multispectral and hyperspectral satellite imagery and ground-truth data to generate accurate habitat maps.
# Utilise ML techniques, including Support Vector Machines (SVM), Random Forest, and deep learning approaches, etc for habitat classification.
# Integrate ecological survey data with spectral indices for enhanced habitat assessment.
# Explore other ML models and satellite data such as SAR.
# Collaborate with multidisciplinary teams, including project partners, to ensure effective data collection, analysis and publication in academic journals and other dissemination and project legacy activities.
This role is initially available until August 2025, with the possibility of extension beyond this date.

# At least a masters degree in a relevant field such as Environmental Science, Earth Observation, Remote Sensing, GIS, ML, or a related discipline.
# Academic and/or industrial experience with EO data analysis and ML techniques.
# Proficiency in using software and tools for spatial data analysis (e.g., ArcGIS, QGIS, Python, ERDAS, ENVI, Google Earth Engine).
# Demonstrated ability to conduct independent research and collaborate within a multidisciplinary team.
# Excellent communication skills for disseminating research findings to both academic and non-academic audiences.
You may also have:
# Experience with habitat mapping models.
# Knowledge of ecological and biodiversity conservation practices, UK Habitat Classification
# Previous experience working with citizen science data and ground-truth calibration and validation.

In addition to salary, you will receive a yearly incremental pay rise, a generous pension, relocation assistance where appropriate, flexible working options including job share and blended home/campus working locations (dependent on work duties), access to world-class leisure facilities on campus, a range of travel schemes and supportive family friendly benefits including an excellent on-site nursery.
We also offer:
# A stimulating, collegiate research environment at the Centre for Environment and Sustainability at the University of Surrey.
# Opportunities to collaborate with leading experts in the field of EO and biodiversity conservation.
# Flexible working arrangements and a supportive professional development program.
About the Project
Space4Nature is an innovative project led by Surrey Wildlife Trust in collaboration with University of Surrey, Buglife, and Painshill Park, funded by People's Postcode Lottery. The project uses state-of-the-art EO technologies, Artificial Intelligence (AI), and ML to map, assess, and restore key habitats in Surrey County. By combining high-resolution satellite imagery with citizen-sourced ecological data, Space4Nature aims to connect fragmented landscapes and enhance biodiversity conservation at a landscape level. The project's goal is to create scalable, evidence-based strategies that empower landowners, policymakers, and conservationists to make informed decisions for habitat restoration, creation and conservation. This approach not only helps reverse biodiversity loss but also supports the resilience of ecosystems in response to climate change and other environmental pressures.