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implementing models that integrate ecological dynamics, species traits, phylogenetic trees, and economic discounting; ● Devising Bayesian or POMDP frameworks to handle uncertainty about species interactions
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: demonstrating the scalability of Lite explanations to handle complex models with sufficient local formal guarantees, establishing the approximation capacity to provide quality solutions with guaranteed
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-Performance Computing for Exascale" contributes to the design and development of numerical methods and software components that will equip future European Exascale and post-Exascale machines. This program is
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will be responsible for performing a sensitivity analysis of SDM modelling applying for five gorgonian species applying BIOMOD2 to the same data set as the one used in Blouet et al. (2024) and extend
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evolution with age. The PhD will be supervised by Carole Peyrin (HDR, CNRS Research Director at LPNC). The PhD student will have access to training courses offered by the doctoral school. Progress will be
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for applying this approach at two sites, where he or she will spend three months each immersed in the daily activities of the communities (participant observation), to better understand their functioning and
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-symmetric complex. This structural symmetry ensures exceptional stability and performance under extreme conditions of temperature, pressure, and magnetic field. In our recent work, we have successfully
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performance. A key objective is to examine how different KG embedding techniques can facilitate complex query answering. These techniques vary in their representation spaces and in how they define relational
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collaborative projects of medium or long duration, during which they learn to manage a project by coordinating activities, distributing work, and making collective decisions. Students generally rely on shared
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of Communities team and interact with its members. The modeling work will also involve collaborations with researchers from CEFE (Montpellier), BIOGECO (Bordeaux), and forest management partners (ONF). Our little