Do you feel concerned by environmental pollution issues? This research will enable optimal deployment of mobile sensors for monitoring air quality in urban environments. Complex urban geometries  and dynamic pollutant dispersion scenarios are the scientific challenges to be met in order to better estimate local air pollution, identify sources and anticipate exposure peaks.
Our previous research has focused on the identification of pollution sources, neglecting the critical aspect of sensor placement . For partial differential equation models, promising approaches based on the structural property of observability of dynamical systems have been proposed . A generic two-stage approach will be studied in the thesis: the development of an infinite-dimensional variational approach for an advection-diffusion model, followed by the finite-dimensional implementation. The results of this thesis will include new sensor placement strategies, quantitative evaluation results in simulation under realistic conditions on a city district in Grenoble and/or Paris, and an in-depth understanding of how Physics-Informed Machine Learning (PIML)  can improve air quality monitoring in urban areas, both in 2D and 3D.
CEA-Grenoble (http://www.youtube.com/watch?v=bCIcNJOzYZY) employs over 2,500 researchers and technicians on a 64-hectare campus in the foothills. The activities of our lab focus on sensor signal fusion through studies in signal and information processing, artificial intelligence, and embedded algorithms, and brings together some twenty experienced research engineers and students from Master 2 to post-doctorate. To join our team, we are looking for a candidate with an applied mathematics profile, a taste for physical models and numerical methods, and good writing skills. You will be co-supervised by Prof. Didier Georges of GIPSA-Lab at Grenoble- Alps University (UGA)(http://scholar.google.fr/citations?user=oF1ahtcAAAAJ&hl=fr). You will also have access to scientific databases, a computing cluster with GPUs and will be trained in the use of a state-of-the-art atmospheric dispersion simulator: Parallel Micro-Swift-Spray co-developed at CEA. Remuneration will be around €2400 (gross) per month during the three years of the thesis. Join us in a unique research environment dedicated to ambitious projects that address today's major societal challenges.
 M. Mendil, S. Leirens, P. Armand, C. Duchenne, “Hazardous atmospheric dispersion in urban areas: A Deep Learning approach for emergency pollution forecast”, Environmental Modelling & Software, Volume 152, 2022
 R. Lopez-Ferber, D. Georges, S. Leirens, “Fast Estimation of Pollution Sources in Urban Areas Using a 3D LS-RBF-FD Approach”, submitted to the European Control Conference 2024
 D. Georges, “Optimal Location of Mobile Sensors for Environmental Monitoring”, European Control Conference (ECC), July 17-19, 2013, Zürich, Switzerland
 M. Raissi, P. Perdikaris, and G. E. Karniadakis. Physics-informed neural networks: A deep learning
framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational physics, 378 :686–707, 2019.