PhD Position in Machine Learning and Remote Sensing for Industrial Pollution Inversion

A PhD position is available in the field of machine learning and remote sensing, focusing on the inversion of industrial stack plumes from satellite images. The project aims to leverage generative models to improve the characterization of pollutants emitted by industrial sites such as steel plants and coal-fired power stations. This research is highly relevant to public health and environmental monitoring, addressing the need for advanced methods to analyze atmospheric pollution. The PhD will be based in Toulouse at ONERA, with supervision from Romain Thoreau (Assistant Professor, AgroParisTech), Pierre-Yves Foucher (ONERA), and Camille Desjardins (CNES). The work is at the intersection of computer science, environmental science, and public health, offering opportunities to develop expertise in generative models, satellite image analysis, and inversion techniques. Applicants should have a strong background in machine learning and remote sensing, with experience in generative models or environmental data analysis considered a plus. The position is ideal for candidates with a master's degree in computer science, environmental science, or related fields who are motivated to contribute to cutting-edge research in pollution monitoring and public health. For more information and the full position description, visit the provided link. The application process and further details are outlined there. The position is supervised by a team from AgroParisTech, ONERA, and CNES, all based in France.