A postdoctoral position is available at Université Laval in Québec City, Canada, focusing on hyperspectral imaging for plant phenotyping. The research will involve processing and analyzing hyperspectral imagery from lab-based systems, developing machine learning and deep learning models (such as CNNs and SVMs) for trait classification, and integrating phenotypic data with genomic datasets (including RNA-seq). The successful candidate will have the opportunity to publish in top journals and present at international conferences, and will have access to state-of-the-art facilities at IBIS, IID, and INAF.

The ideal applicant will have a PhD in Remote Sensing, Computer Vision, Machine Learning, Bioinformatics, or a related field, with strong experience in image analysis and hyperspectral or multispectral data. Proficiency in Python and relevant libraries (PyTorch, TensorFlow, scikit-learn, OpenCV) is essential. The position offers a competitive salary of $60,000 CAD per year (negotiable) with full benefits, and is funded for 2 years. This opportunity is ideal for those passionate about precision agriculture, plant science, and artificial intelligence.

To apply, send your application to [email protected] with the subject 'Application for Postdoc in Hyperspectral Data Analysis for Plant Phenotyping.' For more information, you may refer to the LinkedIn profiles provided.