82 model-driven-development "Integreat Norwegian Centre for Knowledge driven Machine Learning" PhD positions in Denmark
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system configurations, experimental setups and operational data. The scientific ambition is to develop methods that combine physical models and data-driven approaches for adaptive, real-time operation of
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experimentation. The PhD student will develop models and algorithms for the joint design of sensing, communication, inference, and action, and is expected to contribute to a real robotic platform integrating
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experimentation. The PhD student will develop models and algorithms for the joint design of sensing, communication, inference, and action, and is expected to contribute to a real robotic platform integrating
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drought early warning and monitoring system for large-scale river basins. The project will explore both data-driven and model-based approaches for drought predictions, paving the way for a continental high
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observational data, climate model output and regional ocean modelling. Develop and apply a high-resolution hydrodynamic CROCO model, linking model output to the FlexSem model for modelling carbon transport
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Professor Aldo Faisal from Imperial College London involved as international project partner. Research objectives and tasks You will contribute to the development and empirical validation of models
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. The main activities include: Designing machine learning models that can detect unusual, unsafe, or attacked operating conditions. Developing data-driven models that capture how faults and attacks spread
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machine learning models that can detect unusual, unsafe, or attacked operating conditions. Developing data-driven models that capture how faults and attacks spread through a system, and using them to make
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strategic initiative to further strengthen the department’s expertise in digital and AI-driven research methods. Your work tasks • Develop and carry out an independent research project under supervision
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PhD fellowship in fault tolerant quantum algorithms PhD Project in state preparation, observable extraction or noise modelling Niels Bohr Institute Faculty of Science University of Copenhagen