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complex and dynamic environments. The research is driven by applications where current technology is insufficient, such as large-scale biodiversity monitoring in natural environments, smart agricultural and
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. The goal is to enable efficient, robust, and high‑quality situational awareness in complex and dynamic networks, thereby laying the foundation for future autonomous and data‑driven systems. The specific
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20 per cent of full-time. Your qualifications You have a master’s degree in electrical engineering, engineering physics, mechanical engineering, computer engineering, engineering mathematics or have
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of 20 per cent of full-time. Your qualifications You have a master’s degree in electrical engineering, engineering physics, mechanical engineering, computer engineering, engineering mathematics or have
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per cent of full-time. Your qualifications You have graduated at Master’s level in electrical engineering, engineering physics, mechanical engineering, computer engineering, engineering mathematic
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qualifications You have a master’s degree in electrical engineering, engineering physics, mechanical engineering, computer engineering, engineering mathematics or have completed courses with a minimum of 240
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such as autonomous systems, complex networks, data-driven modeling, learning control, optimization, and sensor fusion. The division has extensive collaborations both with industry and other research groups
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application! Your work assignments We are looking for a PhD student to work on the development of novel spatio-temporal machine learning methods. Our world is inherently spatio-temporal, i.e. physical processes
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inherently spatio-temporal, i.e. physical processes around us evolve over both time and space, making spatio-temporal processes and data omnipresent in science and technology, with applications ranging from