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datasets (e.g. ground-based radar measurements and weather station data) Contribute to the development and application of numerical models Assess uncertainties for future sea-level projections Publish
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collaborate on modeling threat actors, developing scalable AI-based monitoring and control systems, and designing strategies to ensure long-term CPS protection. Through a comprehensive training program
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ALMA MATER STUDIORUM - UNIVERSITA' DI BOLOGNA - - DIPARTIMENTO DI SCIENZE GIURIDICHE | Italy | 10 days ago
architectures combining LLMs, symbolic reasoning modules, and structured legal knowledge bases, defining evaluation metrics, and providing technical-scientific and project management support. Where to apply
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such as scalable identification algorithms, uncertainty quantification, and the integration of learning-based models with formal verification. We offer a supportive, inclusive, and collaborative research
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but not limited to: o Quantum error correction, fault tolerance, and resource optimization. o Entanglement dynamics, quantum control protocols, and hybrid quantum-classical algorithms. o Modeling and
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Institut de Recherche en Génie Civil et Mécanique (GeM) | Saint Nazaire, Pays de la Loire | France | 14 days ago
are essential for: validation and enrichment of the predictive models developed in DECISION, definition of local constitutive laws and physically-based boundary conditions for modeling, improved understanding
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years based on satisfactory performance and availability of funding. The Postdoctoral Research Scholar will join a team, under the direction of Prof. Michael Woodford, who studies the cognitive
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crosslink highly diverse bioeconomic production systems and to control them in a knowledge-based, adaptive and largely automated manner. We conduct research in dialogue with society, policymakers, industry
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estimator based on calibration embeddings, reducing calibration complexity. Validation will be conducted on multi-source PSOG datasets with controlled perturbations and diverse operating conditions. The final
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that are commonly used today. Using the improved noise models, machine learning methods will be used to enhance the segmentation of EEG data into auditory signal and background activity allowing for refined control