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Field
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will be part of a leading team in network data science within the Multimedia Computing Group (MMC) in Computer Science. We share a drive to understand and optimize complex systems ranging from social
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Networks for Multi-Scale Urban Energy Systems Your tasks The focus of this research is to design and develop (physics-informed) hierarchical graph neural network architectures that can capture the complexity
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degree / double degree programme Yes Description/content The statistical physics of complex systems is a very broad field ranging from the study of quantum phenomena to the conformational behaviour
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the development of scientific networks in an international environment of the Göttingen campus and beyond. GAUSS provides the organisational and regulatory umbrella for this programme. It was founded in 2005 by
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northern Sweden create enormous opportunities and complex challenges. For Umeå University, conducting research about – and in the middle of – a society in transition is key. We also take pride in delivering
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are undertaking research in aerospace engineering and specifically on the application of distributed propulsion systems on future aeroplanes. Project details: Despite being some of the most complex and efficient
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to the activities of the SIR-V2G project wich primary objective is to address the challenges arising from the large-scale integration of electric vehicles (EVs) into distribution networks by developing smart
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to identify the key factors controlling enzyme performance in complex mixtures. Molecular modelling will be used to interpret experimental trends and support the development of improved process
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and to understand their environmental implications. Project Motivation Hydrogen propulsion is an important enabler of green propulsion in the transition to net-zero. However, this introduces a new and
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well as postgraduate and undergraduate education within areas such as autonomous systems, complex networks, data-driven modeling, learning control, optimization, and sensor fusion. The division has extensive