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materials. This class of materials has unique properties which make them promising candidates for next-generation electronic devices, energy storage systems, sensors, and catalysts. However, they also pose
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candidates for next-generation electronic devices, energy storage systems, sensors, and catalysts. However, they also pose unique challenges from a machine learning perspective, calling for novel machine
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on behavioural syndromes and social networks in dogs and to some extent wolves. The selected PhD student will work with large-scale behavioural data sets using a range of approaches, including heritability
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behavioural phenotypes and social systems develop and evolve. Specifically, the project will focus on behavioural syndromes and social networks in dogs and to some extent wolves. The selected PhD student will
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to their environment through sensors, information and knowledge, and forming intelligent systems-of-systems. Read more: https://wasp-sweden.org/ . The vision of WASP is excellent research and competence in artificial
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characterization methods (potentially including electrochemistry, multiple microscopy modalities, quartz microbalance, impedance spectroscopy) as well as surface modification and characterization and protein
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of segregation, culture, and politics, as well as social network analysis and computational text analysis. IAS is also home of the Swedish Excellence Center in Computational Social Science (SweCSS
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experience with interdisciplinary research networks, and a record of successfully attracting external funding for interdisciplinary research projects. To be able to use computationally intensive methods
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, localization, and sensing, with a focus on developing next-generation multiple-antenna systems while optimizing overall system performance. As a doctoral student, you devote most of your time to doctoral studies
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experience with interdisciplinary research networks, and a record of successfully attracting external funding for interdisciplinary research projects. To be able to use computationally intensive methods