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European Marie Sklodowska-Curie Doctoral Network FADOS. The successful candidate will join a cohort of 17 doctoral students based at 16 research groups in Europe and the UK. About FADOS: FADOS, Fundamentals
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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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array antenna systems for imaging MIMO radar in autonomous driving applications. This work will advance the design and characterization of intelligent devices and environments for wireless communications
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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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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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, computer science and network research. The employment The start date for the position is September 16th. The position is valid during the first half of the fall semester 2025. Salary and employment benefits
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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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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