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affordable and durable long-duration energy storage. The approach is to use hierarchical structures, i.e. complex material layers that can be optimized to specific battery chemistries and flow phenomena from
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, or erroneous data, Data cleaning and generation, Development of enhanced loss functions and information-theoretic methods for optimized data analysis, Machine learning-based image segmentation of tomographic
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. Our research integrates expertise from machine learning, optimization, control theory, and network science, spanning diverse application domains such as energy systems, biomedical systems, material
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decision-support tools for energy-aware planning, predictive maintenance, and resource optimization, -use robotics, autonomous systems, IEC 61499, and digital twins to design and evaluate distributed control
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through a combination of registry-based epidemiological studies, in vitro modelling of biofilm behaviour, and in vivo investigations of antibiotic pharmacokinetics and efficacy. The project is part of
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the optimal design of social support systems. The PhD position primarily concerns the part of the program that studies how AI changes the organization of work and employees. The program currently includes 12
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. Our research integrates expertise from machine learning, optimization, control theory, and network science, spanning diverse application domains such as energy systems, biomedical systems, material
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social marketing within technology and innovation-intensive activities to automation and optimization, to machine design, production and production systems. The Division of Industrial Engineering and
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learning, mathematical statistics, optimization, and robotics. Experience from programming in C/C++ or Python is also meritorious. Willingness to work in an inter-cultural, international, and diverse group
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learning, mathematical statistics, optimization, and robotics. Experience from programming in C/C++ or Python is also meritorious. Willingness to work in an inter-cultural, international, and diverse group