Data assimilation for predicting pollution maps under realistic environmental conditions

Controlling pollutant emissions is one of today’s most pressing environmental challenges. Greenhouse gases (GHGs) and industrial emissions such as methane (CH₄) and hydrogen (H₂) directly impact air quality, safety, and climate change. Yet predicting pollutant dispersion in complex environments like industrial sites remains difficult due to fluctuating wind conditions and obstacles.
This PhD project offers a unique opportunity to develop innovative tools combining high-fidelity simulations based on a Lattice Boltzmann Method (LBM) CFD code with advanced data assimilation techniques, notably the Ensemble Kalman Filter (EnKF). By integrating both fixed and mobile sensors, the candidate will design methods to improve turbulence models, reduce uncertainties, and deliver reliable real-time forecasts. The ambition is clear: support better control of industrial emissions and reduce health and environmental risks.
Bringing together fluid mechanics, high-performance computing, and data science, this project fosters a dynamic, collaborative setting supported by strong academic and industrial partnerships. The candidate will rely on real data from industrial measurement campaigns and contribute to cutting-edge advances in data assimilation. A distinctive feature will be the adaptive control of mobile sensors to optimize data collection and further reduce uncertainties.
This PhD strives for strong scientific impact, contributing to global emission monitoring and decarbonization efforts. Results will be shared through high-level publications and international conferences, ensuring excellent visibility within both academy and industry.
 

IFP Energies nouvelles is a French public-sector research, innovation and training center. Its mission is to develop efficient, economical, clean and sustainable technologies in the fields of energy, transport and the environment. For more information, see our WEB site. 
IFPEN offers a stimulating research environment, with access to first in class laboratory infrastructures and computing facilities. IFPEN offers competitive salary and benefits packages. All PhD students have access to dedicated seminars and training sessions. 
 

Academic requirements    University Master degree (or equivalent) in Mathematics, Computer science or Fluid mechanics 
Language requirements    English level B2 (CEFR), French or willingness to learn French
Other requirements    CFD, Programming skills (Python, C++), numerical analysis, turbulent flows