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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
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of the landscape over time. The LANDIS-II forest landscape disturbance and succession model will be used to perform simulations based on palaeoecological data. The student will collaborate with project researchers
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pressure and temperature levels; - Validate the numerical model of the complete system by comparison with experimental data; - Identify improvement strategies for performance and system robustness. Work
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of their contribution to sea-level rise and the impacts on other components of the climate system. The candidate will also work in close collaboration with the international community of ice sheet modelers within
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arrest at other stages of meiosis. The objective of the PhD project is to understand the role of cyclin B3 during meiosis and early embryonic divisions in the sea squirt Phallusia mammillata and the
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University, is recognized for its expertise in artificial intelligence and formal methods In this stimulating academic context, we will focus on the problem of explainability of artificial intelligence models
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group, which has a long-standing experience in neutrino detection in the deep sea with the ANTARES and KM3NeT experiment, is currently responsible for the construction and the operation of the KM3NeT/ORCA
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) To develop Deep Learning algorithms to significantly speed up probabilistic inference algorithms of current spatial birth-death models 2) To incorporate fossil stratigraphic and spatial information into a new
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is to provide the most **informative** samples (not just the most probable) to facilitate identification and enhance user experience. - **Theoretical Model Improvement:** Understanding a sample's
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element of embedded intelligence lies in the sensor's ability to self-calibrate and, in particular, to adapt its responses and models according to sensor aging and the (sometimes significant) variability