Home / Opportunities / Research Associate in Data Fusion for Forest Monitoring and Modelling

Warning message

Cambridge-based members of C2D3 can log in to view more information about this opportunity.

Research Associate in Data Fusion for Forest Monitoring and Modelling

Closing date: 
Friday, 15 July 2022

Department of Geography

Applications are invited for a Research Associate to join Dr Emily Lines' UKRI Future Leaders Fellowship (FLF) project "Next generation forest dynamics modelling using remote sensing data". This is an interdisciplinary project at the intersection of ecology, remote sensing, and data science. The successful candidate will have experience and expertise in applied data science and machine learning, a demonstrated interest in ecology, terrestrial ecosystems and/or remote sensing of the environment, and be keen to work on complex environmental problems.

Forest ecosystems sequester one third of human-induced greenhouse gas emissions and house 80% of the Earth's terrestrial biodiversity, but face threats including a rapidly changing climate, and deforestation and degradation. Accurate monitoring of the structure and function of forests is crucial for effective understanding of change, but ground data are sparse and irregularly collected, and extracting ecologically meaningful information from remote sensing data is challenging. To advance understanding of forest structure and functioning at large spatial and long temporal scales, and to improve predictions of the future of forests, new, intelligent ways to fuse Earth Observation data with ground data and the output from forest models are needed.

The PDRA will work with a wide range of data collected and collated for Dr Lines' FLF, including extensive co-located ground inventory, Terrestrial Laser Scanning and drone remote sensing data from a range of sites across Europe, and national forest inventory datasets from several European countries. The PDRA will develop new methods to fuse these data with satellite data, and create detailed information about the spatio-temporal variation in forest structural properties across Europe. This will be used to inform new predictive modelling frameworks that flexibly incorporate the full range of data available to predict the future of European forests. The successful candidate will also participate in fieldwork within Europe, and disseminate findings through peer-reviewed publications and presentations at international conferences.

About us

The Cambridge Centre for Data-Driven Discovery (C2D3) brings together researchers and expertise from across the academic departments and industry to drive research into the analysis, understanding and use of data science and AI. C2D3 is an Interdisciplinary Research Centre at the University of Cambridge.

  • Supports and connects the growing data science and AI research community 
  • Builds research capacity in data science and AI to tackle complex issues 
  • Drives new research challenges through collaborative research projects 
  • Promotes and provides opportunities for knowledge transfer 
  • Identifies and provides training courses for students, academics, industry and the third sector 
  • Acts as a gateway for external organisations 

Join us.