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. Integrate hydraulic-hydrologic modeling and surrogate models (e.g., Bayesian Networks) to simulate stormwater behavior under future scenarios. Apply optimization techniques to design and evaluate nature-based
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and research, in and outside academia. About the project The proposed research aims to optimize the design of timber building systems with innovative connections and achieve an extended service-life
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underground conditions. Apply machine learning and AI techniques to enhance model accuracy and optimize design parameters. Contribute to the development of a comprehensive, AI-based design methodology for LUS
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code compilation, which offer a modular approach to domain-specific optimizations. Your project is expected to contribute to cutting-edge research initiatives leveraging, analyzing, and exploiting
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policymakers by providing evidence-based guidance to support informed decisions and ensure children receive interventions that optimize their developmental progress. As a PhD candidate at OsloMet, you will have
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methods to be considered for numerical optimization by an Energy and Emission Management System (EEMS). Data-driven AI methods (e.g. Reinforcement Learning and/or Recurrent Neural Networks) to be considered