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-timescale optimization, and integrated Power-to-X systems. You will drive the development of system-level digital-twin and optimization methodologies, integrating electrolyzer models with renewable generation
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In this PhD position, you will help design the next generation of circular plastics systems by combining hands-on polymer processing with data-driven modelling. The PhD study is a full-time, fixed
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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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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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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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dynamic, crowded environments. As a PhD candidate, you will develop methods that combine data-driven autonomy with formal safety guarantees and validate them in real time through simulation and experimental
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has more than 600 students in its BSc and MSc programs, which are based on AAU's problem-based learning model. The department leverages its unique research infrastructure and lab facilities to conduct