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21 Aug 2025 Job Information Organisation/Company INESC ID Research Field Engineering » Computer engineering Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country Portugal
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Postdoctoral Research Associate in Global Environment Modelling of Soil Organic and Inorganic Carbon
. The project is aimed to improve our in-house developed process-based computer model and use it to represent the soil ecohydrological and biogeochemical interactions across various carbon and nitrogen soil pools
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that can directly take raw, high-dimensional data from experiments or observatories and rapidly infer theory parameters, such as Higgs boson properties at the Large Hadron Collider or neutron star properties
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identified in other WPs. The model construction will be informed by qualitative and quantitative knowledge of supply chain processes through dialogue with stakeholders, and model parameters will be estimated
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of methodologies to detect and locate seismic events recorded in DAS data Development and use of tools to estimate source parameters and fault mechanisms from DAS data Integrate and compare detection thresholds
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TPMS data, high-speed wheel encoders, CAN data, accelerometer data, and acoustics data. In addition to other cloud-based data for weather-friction estimates and crowdsourced vehicle data for estimating
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negotiable, preferably in autumn 2025 or in 2026. Background Northern wetlands emit large amounts of methane (CH4), a potent greenhouse gas. There are high uncertainties in the estimation of wetland CH4
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in the southwest Cordillera. * Integrate geophysical and geochemical information (e.g., seismic, thermal, and compositional models) to constrain crustal rheology and structural parameters within
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general predictive modeling methods, including model design, parameter estimation, sensitivity analysis, and model evaluation. An understanding of data acquisition and curation methods for real-world data
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general predictive modeling methods, including model design, parameter estimation, sensitivity analysis, and model evaluation. An understanding of data acquisition and curation methods for real-world data