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centrifuge experiments. Integrate findings with numerical models for validation and deeper interpretation. Develop innovative experimental apparatus (SolidWorks proficiency is a plus). Disseminate results
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interactions and numerical model- ing. Strong programming skills and running regional or global numerical models (wave, oceanic and possibly atmospheric) are also essential. Applicants should submit a brief
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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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models Foundation models represent a breakthrough in AI, as did the shift from traditional machine learning to deep learning. Numerous models become available in the field of Earth Observation and can be
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Methods, LBM), and HPC. For a selection of possible research areas, see: https://www.math.cit.tum.de/math/forschung/gruppen/numerical-analysis/research/ Responsibilities: Development and analysis of new
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strongly correlated quantum systems. Experience (supported by peer-reviewed publications) with models and analytical or numerical methods/theories such as quantum mechanics, solid state physics, lattice
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UMR 6524, Clermont-Auvergne University. Collaboration with the IMFT (Institute of Fluid Mechanics, Toulouse) for the numerical modelling. Where to apply Website https://emploi.cnrs.fr/Offres/CDD/UMR6524
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and to characterize interactions with fire regimes. Ultimately, these results will also contribute to a combined paleo-modeling approach at the core of the RETROPEST project (https://anr.fr/Projet-ANR
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modeling with deep learning for the analysis of hyperspectral imaging data. The researcher will be responsible for the design and development of numerical models, including neural network architectures
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and numerical models as well as constitutive model calibration and validation based on physical experimental data. Required Qualifications: A successful applicant must have a PhD in Engineering