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series data. Large data sets come with significant computational challenges. Tremendous algorithmic progress has been made in machine learning and related areas, but application to dynamic systems is
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wireless sensor networks as well as research and education within Life Science, smart electronic sensors and medical systems. The Department of Electrical Engineering is an international workplace with
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the real world based on a seamless combination of data, mathematical models, and algorithms. Our research integrates expertise from machine learning, optimization, control theory, and applied mathematics
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, machine learning or similar. Alternatively, you have gained essentially corresponding knowledge in another way. The applicant is expected to have good knowledge of computer science, mathematics, algorithms
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interdisciplinary research on knowledge extraction from social data. Project description The project is in the emerging area of fair social network analysis. In today’s algorithmically-infused society, data about our
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and algorithmic foundations for goal-oriented, semantics-aware communication strategies that enable efficient, intelligent, and adaptive information exchange in joint communication and control. In
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to humans and are accessible to algorithmic techniques while neural models are adaptive and learnable. The aim of this project is to develop models which combine these advantages. The project includes both
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that yield valid statistical conclusions (inference) on causal effects when using machine learning algorithms and big datasets. The project is part of the research environment Stat4Reg (www.stat4reg.se ), and
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addresses research on artificial intelligence and autonomous systems acting in collaboration with humans, adapting to their environment through sensors, information and knowledge, and forming intelligent
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- Control of Communication networks - Markovian Processes - Network Based Localisation / Radio based connectivity - Adaptive bandwidth - Mesh networking - Wireless Sensor Networks - Edge Computing - Time