Machine-learning based monitoring system for cyanobacterial blooms in tropical freshwater reservoirs

Supervisor

This project calls for the development of machine learning methods for the surveillance and monitoring of cyanobacteria in tropical reservoirs. The project will integrate data from multiple length scales (e.g. point samples, optical sensors at fixed locations, colour and/or multispectral imagery from remotely piloted aircraft, and satellite-based remote sensing). There is the opportunity to develop innovative ML methods and combine applied research (to develop useful industrial applications) and fundamental research (to create new ML algorithms and incorporate innovative approaches to handle, for instance, sparsely labelled data, multiple input modalities, etc). There is also the opportunity to develop new optical or electrical sensors for in-situ measurements.

20 Apr 2026 - 22 Oct 2029

N/A