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Your position We are seeking a highly motivated PhD student to join our interdisciplinary research team. You will have the opportunity to apply cutting-edge quantitative methods and modeling
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Institut de Recherche pour le Développement (IRD) | Sete, Languedoc Roussillon | France | 14 days ago
France. The unit includes around 300 staff members (researchers, faculty, engineers, PhD students), mainly based in Sète, Montpellier and Palavas-les-Flots, with numerous international collaborations
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Institut de Recherche pour le Développement (IRD) | Sete, Languedoc Roussillon | France | 8 days ago
of IRD, Ifremer, CNRS and the University of Montpellier, it is a major center for marine ecology research in France. The unit includes around 300 staff members (researchers, faculty, engineers, PhD
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scalable multi-fidelity methods capable of merging information coming from models with mismatched parameterizations and heterogeneous data structures. The PhD will investigate i) how to define common latent
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products will subsequently inform improved basal hydrology parameterizations within ISSM, linking observed water dynamics to ice-sheet motion. The ideal candidate for this PhD position has a Master’s degree
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, the environment and ecology, transportation, robotics, energy, culture, and artificial intelligence. Overview of the CNRS as an employer: https://www.cnrs.fr/fr/le-cnrs Presentation of IRISA as the host laboratory
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and field monitoring work performed by a PhD student at LIST and other researchers in LAFI, and extend the existing Vegetation Optimality Model (VOM, https://vom.readthedocs.io ) to test the following
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the brittle-ductile transition depth. This transition depends on the rheological parameterization of the cold lithospheric mantle, approximating brittle deformation by a depth-dependent yield strength and
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discrete black box combinatorial optimization problems (https://arxiv.org/abs/2510.01824 ). In this work, we parameterize a multivariate autoregressive generative model for generating solutions. By sampling
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and field monitoring work performed by a PhD student at LIST and other researchers in LAFI, and extend the existing Vegetation Optimality Model (VOM, https://vom.readthedocs.io ) to test the following