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studies of physics‑informed control of mobile manipulators, data collection from real and simulated machines, and model development and testing in simulated environments. The project offers close
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the implications for forest ecosystem service supply. The main methodological approach applied will be forest landscape simulation modelling using the model iLand. The work is embedded in the BETA-FOR project (https
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of energy consulting services within the infrastructure of a Data Space Analyze data from public and private data sources in relation to the energy consumption of the built environment Validate the modeling
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approach (CPlantBox) to mechanistically simulate plant growth, plant-soil interactions and the rhizosphere microbiome. You will contribute to model development and apply it to disentangle the role of root
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on gross and net carbon fluxes for sites where such data are available. Additionally, the researcher may perform simulations with a specific model to analyze the contributions of various factors (climate, CO
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the effects of these factors at the tissue level. The position will also involve simulating and optimizing complex models that integrate biological and physical data using advanced methods, such as physics
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 4 days ago
. Studying literature Scanning rose flowers at different growth stages Implementing and testing neural shape models Writing research reports Where to apply Website https://jobs.inria.fr/public/classic/en
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charging) Predictive charging planning algorithms Railway Smart Pricing models Techno-economic evaluation versus the traditional catenary model This is applied research with validation in simulated
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provide detailed information on local deformation mechanisms at the microscale, while numerical simulations and data-driven approaches will enable the development of predictive models capable of linking
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differential equation models of bacterial persistence. A particular challenge, both for simulation and for machine learning, lies in the high dimensionality of these equations, which causes grid-based numerical