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well as next-generation ecological models that take uncertainty into account. The https://leca.osug.fr (LECA) is part of the University of Grenoble Alpes and the CNRS in France. Grenoble is located close to
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the flexibility and power of NNs with the ability of LMMs to robustly learn from structured and noisy (non i.i.d.) data, applying them on the prediction of both plants and human phenotypes. These models will
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selectivity and permeability and ultrahigh water permeability combined with high salt rejection. The objective of this work is to construct atomistic models of MOFs/Polymers and Artificial Water-Channel
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and motivations) and then to combine modelling and complementary experiments to refine our understanding of the phase transition mechanisms at play. To do this, the postdoctoral researcher will use
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, takes place in the frame of the SF-PLANT project of PEPR SupraFusion which aims at assessing the impact of high temperature superconductors in the design of fusion power plants. The prediction of plasma
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to the specification and analysis of data from major space missions (SMOS, Biomass, Venµs, Trishna) and develops models capable of describing and predicting the evolution of continental surfaces under various pressures
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potential. This post-doctoral project aims to develop a predictive model of tumor growth based on artificial intelligence (AI) approaches capable of integrating data from morphological MRI and MRS
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developed using finite element analysis (FEA) between LMGC, ICube and LEM3 Labs to model the behaviour of Wharton's jelly samples in an ex vivo and in vivo context. Predictive tools, based on previous models
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by the CNRS, the postdoctoral researcher will be responsible for contributing to the development of advanced methodologies for predicting crystal structures (CSP) based solely on their chemical
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organ transplantation. HLA-Epicheck is a predictive model of the antigenicity of polymorphic amino acids on the surface of HLA antigens, relying on dynamic structural data. Four tasks are identified. Task