modelling (validation): 14
members
-
J. Le Fur
Agent-Based Modelling of Complex Marine and Terrestrial EcoSociological Systems
(Conception and development of information systems dedicated to formalization of knowledge)
-
E. Paradis
Biostatistics and Modelling in Evolutionary Biology and Ecology
(Application to biodiversity and anthropogenic impacts on ecosystems)
-
R. Djidjou Demasse
Data Analysis and Mathematical Models for Sustainable Control of Antibiotic Resistance
-
L. Berti-Equille
Data Quality Awareness for Computational Sustainability
(Statistical machine learning, data cleaning, probabilistic inference, data fusion, data quality, data management)
-
P. Lebellegard (retired 2023)
Development of Seismic Monitoring Networks in the South Pacific Islands
-
A. Dezetter
Hydrosystems and Water Resources Modelling
(Integrated approach to vulnerable water resources under changes and uncertainty)
-
N. Barrier
Improving computational efficiency of multi-level, multi-scale marine environment models
(oceanography, hydrodynamics, ecology, trophic chain)
-
R. Hostache
Monitoring and Integrated Modelling of Hydrological Resources and Risks (Floods and Droughts)
(remote sensing data analysis, assimilation and modelling)
-
A. Drogoul
Multi-Scale Agent-Based Modelling and Simulation for Research and Operational Decision Support
(Direction in development and computer science research)
-
M. Mangeas
Operational Application of Deterministic and Stochastic Mathematics to Spatio-Temporal Environment-Health Issues
(optimization, hybrid system, neural network, genetic algorithms)
-
F. Niño
Processing and Dissemination of Satellite Altimetry Data for Water Resources and Coastal Management
(radar altimetry, deep learning, numerical simulation)
-
C. Lett
Quantifying Connectivity and Resilience of Marine Networks Under Climate Change
(Individual based models of recruitment and larval stages passive dispersal)
-
P. Ploton
Vulnerability of African Tropical Forests to Global Changes
(satellite/aerial sensing and modelling of canopy trees structure biomass and phenology)
-
J. Guérin
[IN CONSTRUCTION - NOT VALIDATED] Developing Machine Learning Approaches to Enhance Global Resilience to Climate Change
(ML experiments design, runtime monitoring, robustness assessment, optimisation and evaluation)
