Carole Delenne
About Carole Delenne
Carole Delenne is a researcher and educator at Inria and Université de Montpellier, specializing in hydrodynamic modeling and flood risk assessment. She has over 17 years of experience in academia and focuses on the development of automatic mapping algorithms and data fusion techniques.
Work at Inria
Carole Delenne has been a member of the Inria Lemon team since 2014. Located in the Région de Montpellier, France, she conducts research at HydroSciences Montpellier. Her work focuses on hydrodynamic modeling, with a particular emphasis on flood risk and water networks. Delenne's contributions to the team involve applying advanced modeling techniques to address complex water-related challenges.
Education and Expertise
Carole Delenne holds a doctorate in image processing from AgroParisTech, where she studied from 2003 to 2006. Prior to this, she earned a Diplôme d'ingénieur in Mathematics from the Institut national des Sciences appliquées de Toulouse, completing her studies from 2000 to 2003. Her academic background supports her specialization in the parameterization of models for large-scale applications, particularly through the development of automatic mapping algorithms and data fusion techniques.
Background
Delenne has been a Maître de conférences at Université de Montpellier since 2007. With 17 years of experience in this role, she teaches primarily in the fields of hydraulics and applied mathematics. Her position at the department of Génie de l'Eau at Polytech Montpellier allows her to integrate her research insights into her teaching, providing students with a comprehensive understanding of theoretical and practical aspects of water engineering.
Research Focus
Carole Delenne's research at HydroSciences Montpellier centers on hydrodynamic modeling, particularly related to flood risk and water networks. Her work involves the development of innovative modeling techniques that are crucial for understanding and managing water resources effectively. Delenne's expertise in automatic mapping algorithms and data fusion techniques enhances her research capabilities in large-scale applications.