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transportation networks but also the case of wind farms, solar grids and IoTs. Consequently, developing and using machine learning tools to process these graph data is more important than ever. Such a tools need
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available validated DEM simulation models and enriched by operational equipment performance data. To this end, physics informed machine learning techniques will be used to bring model data and real data
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nonlinear interactions at the origin of such extreme events. In this project, we will explore the use of cutting-edge scientific machine learning framework that blends deep learning with physics-based
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. On this PhD project you will develop novel systems biology methods employing control and analysis of dynamical models, and machine learning models, in particular neural networks. The developed methods will be
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promising solutions. This project aims to enhance SDB accuracy through deep learning pan-sharpening and physics-informed machine learning techniques. These methods will be tested in two regions worldwide and
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-box nature of machine learning models, you will bring clarity and predictability to your models. Trust will be earned as you evaluate the robustness of your models in different scenarios, identifying
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, or a related field, and a strong interest in human-computer interaction. Experience in machine learning, natural language processing, and deep learning is also a plus. Requirements Specific Requirements
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or at least some interest in machine learning, preferably techniques for management/adaptation of systems. Programming experience in MATLAB/Python or C/C++, preferably in relation to radar signal processing. A
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interest in machine learning, preferably techniques for management/adaptation of systems. Programming experience in MATLAB/Python or C/C++, preferably in relation to radar signal processing. A curiosity
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discovery processes, LLMs help maintain an organized and dynamic knowledge base, supporting continuous learning and decision-making within organizations and beyond. This PhD research will also investigate