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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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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 | 9 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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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 | 13 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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(Lua/Java), agent behavior modeling, event handling, and API-based integration with external AI systems. Experience with distributed systems, reinforcement learning, or simulation environments (e.g
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build upon the existing Alpha framework (originally developed to model protein quality, see https://alpha-tool.eu ) and further develop it as a research-oriented modelling and demonstration platform
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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