This position seeks a motivated researcher to contribute to an interdisciplinary initiative focused on modeling forest growth through remote sensing and machine learning. Hosted at a leading medical school in collaboration with NUS and NParks, the project emphasizes real-world ecological data and scalable computational methods.
Key Responsibilities
- Collect and manage remote sensing data from UAV missions conducted across diverse forest environments
- Design and implement machine learning models for biodiversity classification and structural analysis of forest canopies using LIDAR data
- Develop robust backend systems to process, store, and serve UAV-derived datasets
- Create an interactive data portal enabling stakeholders to submit inputs and visualize results, following best practices in software engineering
- Coordinate technical efforts across NTU, NUS, and NParks to ensure alignment and progress
Qualifications
Candidates should hold a Master’s or Honours degree with demonstrated experience in data science, remote sensing, or machine learning. A solid foundation in software engineering—either through academic projects or industry roles—is essential. Strong communication skills are required to support collaboration across technical and institutional boundaries.
Professional Development
The researcher may enroll part-time in a graduate programme at NTU within the School of Electrical and Electronic Engineering or a related field, supporting long-term research goals while contributing to an impactful environmental project.
