To detect pollutants in the air, we are developing compact devices that probe the chemical reactivity of gaseous pollutants using coloured microbeads. In order to detect whether these microbeads have come into contact with a pollutant, their colorimetry needs to be analysed using on-board optical microcosopy or spectral analysis. We want to develop a version of this equipment that could be easily deployed.

Background:
To detect pollutants in the air, we are developing compact devices that probe the chemical reactivity of gaseous pollutants using colored microbeads [Mugherli et al., Lab-on-a-Chip 2020; Guittet et al., Journal of Sol-gel Science and Technology 2023].

Main objective:
This project aims to improve and validate measurement devices, in conjunction with a post-doctoral researcher. We have developed on-board electronics combined with AI-based firmware software for continuous, real-time analysis of colored microbeads. We aim to improve the technique by exploring different AI classification approaches, as well as adding additional real-time functionalities (streaming video, unsupervised learning, etc.).

Main tasks:
(i) Comparison of different learning techniques
(ii) Real-time user interface (video and image processing)
(iii) Optional miniaturization of the electronic board
(iv) Testing new approaches to microbead capture

Qualities sought & to be developed :
R&D skills Python, C++, linux
Professional skills Autonomy, Efficiency, Creativity, Communication, Writing
Interests Embedded systems, Environment, Air quality