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of Psychology at Princeton University. Research in the lab focuses on the cognitive, computational and neural bases of cognitive control, using a combination of behavioral, neuroimaging, and AI modeling methods
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for the analysis of hyperspectral imaging data applied to pictorial layers, based on coupling physical radiative transfer models (two-flux and four-flux approaches) with machine learning methods. The researcher will
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-based models that optimise and control pharmaceutical manufacturing processes effectively. Your main responsibility is to develop and enhance discrete element models (DEM), integrating physics-based
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using methods such as Dynamic Mode Decomposition with control (DMDc). You will also assist in the development of predictive control approaches based on reduced-order models, and contribute to workflow
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shaping will be central to the study. The numerical model will be based on the boundary element method (BEM) and semi-analytical approaches developed at I2M. The experimental proof-of-concept will leverage
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necessary. The modelling is particularly complex in a context where the environment is controlled by local discontinuities, i.e., fractures or preferential pathways, which can carry the fluid over large
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 4 hours ago
(GMAO), is a comprehensive Earth system model and data assimilation system. The GMAO assimilates aerosol optical depth (AOD) observations from space-borne (e.g. MODIS, VIIRS) and ground-based (AERONET
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the MediTwin project, which aims at advancing patient-specific digital twins for medical applications by combining physics-based modeling, data assimilation,and efficient computational pipelines (https://www
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Your Job: The objective of this thesis is to model the European buildings sector in our European energy system model, based on the framework ETHOS.FINE, and to optimize the transformation pathway
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lead advanced statistical analyses integrating ecological datasets with spatiotemporal modelling frameworks. The work will contribute with evidence-based data to development of ecosystem-based