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prediction to process optimization. The focus of this PhD project is to develop and apply machine learning methods across three interconnected tasks: 3D microstructure characterisation. The student will
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for hypergraphs and partially ordered sets (POSets), funded by the Swedish Research Council. This project is concerned with saturation problems for two classes of combinatorial objects: hypergraphs and posets
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expertise from control theory, machine learning, optimization, and network science, spanning diverse application domains such as energy systems, biomedical systems, neuroscience, and safety and security
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, such as heterogeneity of data sources and communication constraints. By leveraging tools from statistical signal processing, machine learning, optimization, and mathematical modeling, the project aims
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algorithms. Our research integrates expertise from control theory, machine learning, optimization, and network science, spanning diverse application domains such as energy systems, biomedical systems
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dynamics. Particular emphasis is placed on opinion dynamics as well as distributed problems in coordination, optimization, and learning. The research encompasses both theoretical and computational aspects
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‑mining and machine‑learning methods. The expected scientific outcome is to establish guidelines for identifying and optimizing promising electrolyte materials and to support the development of future
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screening approaches. The PhD student will develop and apply analytical workflows to characterize complex food matrices. The project includes i) developing and optimizing screening workflows; ii) improving
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focuses on leveraging zebrafish as a model organism to develop and optimize genetic tools through a directed evolution pipeline, with significant therapeutic and industrial applications. Key
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relevant not only for the organizations developing, training, or optimizing AI models, but in particular for users of the software products that inform and impact the policies that will regulate the AI