This PhD project enhances an adaptive electronic nose by optimising multi‑sensor arrays and applying AI‑based pattern recognition to enable real‑time water quality monitoring, environmental sensing, and chemical threat detection across diverse field settings.
For this project, I am seeking a PhD candidate to improve the adaptive electronic nose platform for real-time water quality assessment, environmental monitoring, and chemical threat detection. The project builds on a modular, low-cost multi-sensor system capable of integrating new MOS, MEMS, electrochemical, and NDIR sensors.
The candidate will optimise sensor arrays, implement drift correction and environmental compensation techniques, and apply AI based pattern recognition to classify chemical fingerprints from pollutants and chemicals. The system’s real-time dashboard and cloud-based data architecture support field deployment for water treatment sites, natural waterways, indoor air quality assessment, and rapid chemical threat screening.
School of Science
Water quality
1871 | 2911
