Research Associate, Geographical Information System and Remote Sensing

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Application is invited to work on a multiple-year interdisciplinary project entitled “Forest Restoration Digital Companion: A Social-Ecological-Technical Systems Approach”. The project, which collaborates across the fields of forest ecology, remote sensing, machine learning and spatial data science, will develop a suite of models and digital tools consolidated into a single platform to assist forest restoration work end-to-end in forecasting restoration outcomes, site prioritization, recommending actions and logistics, and monitoring for adaptive feedback. The project aims to improve our understanding of forest regeneration in Singapore and the efficacy of restoration approaches in achieving carbon sequestration and biodiversity conservation targets. 

The project is a collaboration among the Department of Biological Sciences at National University of Singapore (NUS), the Department of Geography, NUS, the School of Electrical and Electronic Engineering, Nanyang Technological University (NTU), and the Asian School of the Environment, NTU. Successful candidates should be scientific-driven, able to perform field work, and manage the project tasks and outputs independently. In addition to the respective research scope described below, the position will work collaboratively with other project team members to integrate results from the project tasks to develop a digital tool GUI web-hosted on GPU server, with a companion app for forecasting and reporting forest recovery status.

This full-time Research Associate (with Master’s degree) position will ideally also be enrolled as a part-time PhD student at the NUS Department of Geography, working with A/P Wang Yi-Chen and Dr. Chua Siew Chin on the geospatial related tasks of the project. The RA is required to reconstruct land use histories and vegetation cover using GIS and remote sensing, quantify forest patch connectivity, incorporate stakeholder’s views to perform multi-criteria decision analysis, as well as contribute to modelling forest growth and ecological processes at stand to landscape scales.