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to urban water systems. • Develop models, and predictive tools for hydrological analysis and urban water management. • Design analytical approaches to improve water resources management and urban
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challenge is therefore to develop efficient surrogate models capable of rapidly predicting macroscopic mechanical properties directly from microstructural descriptors while preserving the underlying physical
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). The proposal lies at the intersection of digital twins, AI techniques, and predictive model development, proposing an integrated and scalable ecosystem capable of enabling new energy management
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NOBM using prognostic fluxes predicted by the GISS climate model in order to characterize the dust pathways, the timing and magnitude of dust-iron deposition events, the regional and temporal variations
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physics-informed machine-learning models for binding affinity predictions in rational small-molecule drug design. The models will allow prioritisation of candidates from hit discovery through to lead
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Water consumption under various application parameters Impact of dosing changes on crop quality Economic efficiency Impact on variability of soil properties Predictions based on AI models Supervisor
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successful candidate will dedicate their efforts to the following specific research objectives: 1) Developing models for predicting the thermal runaway (TR), venting, and jet fire in a single cell with
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relational database environments Apply and evaluate methods from causal inference (e.g., confounding control, bias assessment, sensitivity analyses) Apply machine learning approaches for predictive modeling
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. - Conduct high-throughput serum proteomic analyses and integrate molecular datasets. - Validate candidate biomarkers in independent cohorts. WP3.2 – Integrated predictive modeling: - Develop integrative multi
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. The lack of knowledge is related to the models that should be used to auralize UAM in urban environments: new models are needed to predict noise exposure in urban cities. Traditional aircraft noise studies