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induced seismicity. Current models remain limited by the scarcity, heterogeneity, and noise of available data, as well as by incomplete knowledge of the subsurface. Physics-Informed Neural Networks (PINNs
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refines algorithms and workflows for crop and pasture monitoring, modeling, prediction, and decision support and automation; Supervises graduate research assistants and student interns working
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predictive control, optimization-based decision frameworks, and data-driven performance modelling. The overall goal is to develop computational methods that enable efficient and intelligent operation of wind
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integration of the SUMMA system for hydrological predictions. Application of the models to the Alcácer do Sal region, located in the Setúbal District Contribute to the development of a prediction and forecast
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model biases, and identify sources of predictability. The project will involve; 1) rigorous interrogation of NOAA GFDL's CM4X simulation output with respect to coastal sea level variability and relevant
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, for the project “PREDICT-OAE project (From data to PREDICTion: modelling temporal ecological–biogeochemical links under Ocean Alkalinity Enhancement”, financed by internal funds from CIÊNCIAS and FCiências.ID
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physical agent-based models, as well as the integrations of omic information to validate model predictions and developed in the context of the HPC environments at the BSC and at other HPC centres in Europe
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Distributed, robust and adaptive model predictive control (MPC) School of Electrical and Electronic Engineering PhD Research Project Self Funded Dr P Trodden Application Deadline: Applications
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and implement multimodal retrieval with re-rankers for robust profile selection. Design and train advanced AI models for digital twin: 3D model learning, prediction models from imaging and molecular
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generation and predictive modeling by measuring the conductivity and permittivity of diverse electrolytes. The research will be structured into four key phases: (i) the design, fabrication, and validation