Research Fellow — AI for Coastal Vegetation & Flows

National University of Singapore; Singapore; On-site; SGD 60,000 - 90,000; Full time

Job summary

National University of Singapore is seeking a Research Fellow to lead AI-driven visual monitoring of coastal vegetation and flows. You will design wave-flume experiments and develop deep learning models to infer water velocity and turbulence from vegetation motion.

The role involves integrating RGB-D data with LiDAR, mapping vegetation structure and bed changes, and validating estimates against measurements while ensuring reproducible workflows and documentation.

Qualifications

• A PhD in engineering, computer science or a related field.
• Strong Python programming and machine learning skills with model validation on measurement data.
• Experience with computer vision for video, depth imagery or 3D point clouds and reproducible analysis.
• Familiarity with coastal hydrodynamics, flume experiments or coastal field surveys is desirable.

Responsibilities

• Lead development of visual monitoring methods for the VASE-R project.
• Design wave-flume experiments pairing video of vegetation with acoustic Doppler measurements.
• Develop and test deep learning models to estimate water velocity and turbulence.
• Create computer vision methods combining RGB-D images with LiDAR and map vegetation structures.
• Validate flow estimates against measurements and document analysis protocols.

Skills

Python programming

Machine learning

Computer vision

Data analysis

Education

PhD in engineering/computer science or related field

Tools

LiDAR surveys

3D point clouds

RGB-D imaging

Acoustic Doppler measurements

Job description

National University of Singapore is seeking a Research Fellow to lead AI-driven visual monitoring of coastal vegetation and flows. You will design wave-flume experiments and develop deep learning models to infer water velocity and turbulence from vegetation motion.

The role involves integrating RGB-D data with LiDAR, mapping vegetation structure and bed changes, and validating estimates against measurements while ensuring reproducible workflows and documentation.