Hyperspectral Imaging for Detection and Quantificati...

Hyperspectral Imaging for Detection and Quantification of Microorganisms in Aquatic Environments

Master internship – Brussels; more than two weeks ago.

Water quality monitoring is critical for aquaculture, environmental protection, and public health. Traditional methods for detecting microorganisms and algae often rely on manual sampling and laboratory analysis, which are time‑consuming and unsuitable for continuous monitoring. Hyperspectral imaging offers the potential to capture rich spectral signatures that can be used to identify and quantify biological content in water.

Objectives

This project aims to investigate the feasibility of using hyperspectral imaging combined with machine learning to detect, identify, and quantify microorganisms in water, with a long‑term perspective toward deployment in aquaculture ponds or natural water bodies. The project will establish a foundation for more complex research.

Research Questions

• Can hyperspectral signatures be used to distinguish between different types of microorganisms or algae?
• What level of quantification accuracy is achievable under realistic conditions?
• How robust are models to changes in water quality and environmental conditions?
• Can spectral information be combined with spatial and temporal cues to improve reliability?

Methodology

The student will:

• Review existing work on hyperspectral imaging for water analysis and biological sensing.
• Collect or work with controlled hyperspectral datasets of water samples.
• Develop preprocessing and feature extraction pipelines.
• Train and evaluate machine learning models for identification and quantification tasks.
• Explore extensions toward monitoring aquatic animal presence or movement using spectral and spatial cues.

Internship Details

• Type of internship: Master internship
• Duration: 6–9 months
• Required educational background: Bioscience Engineering, Chemistry/Chemical Engineering, Computer Science, Electrotechnics/Electrical Engineering

Application Process

Please include a resume, motivation letter, and current study information. Incomplete applications will not be considered.

The reference code for this position is 2026-INT-078. Mention this code in your application.

Supervising scientists: Hyun‑su Kim (Hyun-su.Kim@imec.be) and Tien Nguyen (Tien.Nguyen@imec.be).