Post-doctoral research project conducted by L@bISEN, as part of the SIIRI project funded by the Bretagne region, under the axis of 'collaborative innovation at the intersection of industries.' This project involves renowned companies operating in the field of optical solutions, including the design, manufacturing, and maintenance of technological systems.
Position Profile:
Application Context:
The use of Artificial Intelligence (AI) in spectral imaging offers promising opportunities to enhance current solutions in the agri-food industry. Automated models are becoming increasingly popular as they allow systems to learn from data without explicit programming. This growing adoption is due to their user-friendliness, making AI accessible even to non-experts. These techniques are being explored in hyperspectral imaging to improve precision, efficiency, speed, and reliability in the agri-food industry.
Objective:
Develop an AI engine with automated-learning model capabilities to interpret hyperspectral data in real-time according to various needs. This engine will provide versatility to the control machine, enabling it to adapt to various applications in the agri-food industry (foreign object detection, quality control, etc.). The integration of these automated-learning models for hyperspectral data represents a major innovation, opening new perspectives for the analysis and exploitation of these complex and information-rich data. This approach aims to maximize the potential of hyperspectral imaging and improve interpretation model performance in a timely manner. This capability represents an unprecedented advancement in the context of industrial solutions based on rapid hyperspectral imaging.
Keywords: hyperspectral imaging, automated-learning models, machine learning, deep learning, data interpretation, complex data, adaptability, accessibility, optimization, agri-food industry.
The candidate should have:
Benefits:
To apply:
Please submit the following documents:
via email to the following addresses:
The applications remain open until the position is filled.
References :
(1)Ravikanth, L., Jayas, D.S., White, N.D.G. et al. Extraction of Spectral Information from Hyperspectral Data and Application of Hyperspectral Imaging for Food and Agricultural Products. Food Bioprocess Technol 10, 1–33 (2017). https://doi.org/10.1007/s11947-016-1817-8
(2)H. Su, Z. Wu, H. Zhang and Q. Du, "Hyperspectral Anomaly Detection: A survey," in IEEE Geoscience and Remote Sensing Magazine, vol. 10, no. 1, pp. 64-90, March 2022, doi: 10.1109/MGRS.2021.3105440
(3)Shubhra Kanti Karmaker (“Santu”), Md. Mahadi Hassan, Micah J. Smith, Lei Xu, Chengxiang Zhai, and Kalyan Veeramachaneni. 2021. AutoML to Date and Beyond: Challenges and Opportunities. ACM Comput. Surv. 54, 8, Article 175 (November 2022), 36 pages. https://doi.org/10.1145/3470918
(4)Chang C-I. Hyperspectral Data Processing : Algorithm Design and Analysis. Hoboken NJ: John Wiley & Sons; 2013. http://www.books24x7.com/marc.asp?bookid=46767. Accessed June 1 2023
(5)Hu, X.; Xie, C.; Fan, Z.; Duan, Q.; Zhang, D.; Jiang, L.; Wei, X.; Hong, D.; Li, G.; Zeng, X.; Chen, W.; Wu, D.; Chanussot, J. Hyperspectral Anomaly Detection Using Deep Learning: A Review. Remote Sens. 2022, 14, 1973. https://doi.org/10.3390/rs14091973
(c) GdR IASIS - CNRS - 2024.