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) Scale up forest dynamics predictions from stand to landscape level. As the PhorEau model cannot be run in a fully spatially explicit manner at large spatial scales, the PhD candidate will interpolate
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(s) Number of offers available1Company/InstituteLaboratoire "Atmosphères et Observations Spatiales"CountryFranceCityGUYANCOURTGeofield Contact City GUYANCOURT Website http://www.latmos.ipsl.fr STATUS
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on spatial remote sensing and geographic information systems (GIS). The new mathematical models developed as part of the Math-Vive PEPR will serve as a basis for building predictive analysis models and
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investigate how intra-tumour heterogeneity (ITH) shapes the immune microenvironment in primary and metastatic breast cancer. Using spatial transcriptomics and mouse models, the candidate will map immune niches
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will involve (but not be limited to) applying sophisticated mouse genetic models of cancer and conditional gene targeting, single-cell RNA-seq, spectral flow cytometry, multiplex imaging and spatial
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the spatial and temporal transferability of the models. • Projection of the relationships obtained in future climate scenarios using ADAMONT projections to assess how gravitational crisis episodes will evolve
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to experimentally test predicted models would be appreciated. Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UPR2357-PATACH-008/Default.aspx Work Location(s) Number of offers available1Company
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of environmental seismology, an emerging field focused on interpreting seismic signals generated by surface processes. This interdisciplinary PhD project aims to integrate hydraulic measurements, physical models and
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frequent cloud contamination. This scale mismatch prevents a coherent representation of radiative–thermal processes at the urban scale. This PhD will develop physics-informed deep learning models for data
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Inria, the French national research institute for the digital sciences | Toulouse, Midi Pyrenees | France | about 1 month ago
-Stokes, RANS and ZDES model equations. The objective of this PhD is to develop adaptive techniques compatible with high order boundary approximations. The aim is to be able to efficiently reduce the error