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from geomatics, spatial analysis, multi-agent modeling and simulation, and laboratory work (soil and surface formation studies; host of the Solsup platform of the EMerode service unit). General
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tools on real data will allow a better understanding of gas dust and dusty-wind CND interaction, the use of resolved dust structures to constrain the BLR geometry and eventually improved modeling of BLRs
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and physics, Materials Mechanics, Geotechnics, and Civil Engineering Proven skills in laboratory and/or field experimentation Potential aptitude in numerical modeling Ability to work in a team and in
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, residents). RMeS is structured around 2 independent research teams: REJOINT (formerly STEP) and REGOS (see organizational chart ). These 2 teams still benefit from our 4 open technological platforms: SC3M
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. Tasks 1. Modeling and analysis of uncertainties The first phase of the research will focus on identifying, structuring, and integrating major sources of long-term uncertainty that influence strategic
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are widely used in modern life due to their strength and durability, with applications ranging from construction to packaging. However, their long-lasting nature also poses environmental challenges, as they
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of teaching and research, the FSTM seeks to generate and disseminate knowledge and train new generations of responsible citizens in order to better understand, explain and advance society and environment we
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of the variability and uncertainty of simulated outputs • an explicit quantification of prediction error • an interpretable and controllable structure (e.g., Gaussian processes, …) 2. Model industrial system
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over the course of the project. References: - Deneu B et al (2021) Convolutional neural networks improve species distribution modelling by capturing the spatial structure of the environment. PLoS Comput
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by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The successful candidate will use the poroelasticity models developed in the M3DISIM team