Research Fellow (AI and Visual Monitoring of Coastal Vegetation and Flows)

Interested applicants are invited to apply directly at the NUS Career Portal. Please note your application will only be processed if you apply via NUS Career Portal.

NUS Career Portal link: https://careers.nus.edu.sg/job/Research-Fellow-(AI-and-Visual-Monitoring-of-Coastal-Vegetation-and-Flows)/34945-en_GB/

We regret that only shortlisted candidates will be notified.

How much can a video of swaying seagrass tell us about the flow around it? Join Asst Prof Jiarui (Gary) Lei’s team in Civil and Environmental Engineering at the National University of Singapore. You will lead the development of visual monitoring methods for the CFI Singapore VASE-R project (V2-P9).

Design wave-flume experiments that pair video of vegetation motion with acoustic Doppler measurements. Develop and test deep learning models that estimate water velocity and turbulence from that motion.

Develop computer vision methods to combine colour and depth (RGB-D) images with LiDAR surveys. Map vegetation structure and bed changes, including scour around mangrove roots and erosion at seagrass patch edges.

Validate flow estimates against flume and field measurements, and bed reconstructions against measured bed profiles. Quantify uncertainty and document the analysis code and field protocols so other teams can repeat the measurements.

Apply through NUS Careers with a CV and a short note on your research interests. For enquiries, contact Asst Prof Gary Lei at Sign in to view email.

Qualifications

1. A PhD in engineering, computer science or a related field.
2. Strong Python programming and machine learning skills, including experience developing and validating models on measurement data.
3. Experience with computer vision for video, depth imagery or 3D point clouds, and the ability to design and communicate a reproducible analysis.
4. Familiarity with coastal hydrodynamics, flume experiments, acoustic Doppler measurements or coastal field surveys is desirable.