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rapidly evolving areas such as autonomous systems, data-driven modeling, learning-based control, optimization, complex networks, and sensor fusion. Research at the division is characterized by close
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releases, and greenhouse gas flux estimation. Current approaches struggle to assimilate data from heterogeneous sensor networks, are too computationally demanding for real-time deployment, and lack reliable
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teaching in key and rapidly evolving areas such as autonomous systems, data-driven modeling, learning-based control, optimization, complex networks, and sensor fusion. Research at the division is
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that does not reflect local reality and thereby create unfair comparisons. Your work will include measurements of greenhouse gases (CO2, CH4, N2O) using drones and various sensors, as well as remote sensing
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distributed wireless systems" which is conducted in collaboration between Linköping University (LiU) and Lund University (LU). Read more here: https://elliit.se/project/machine-learning-for-sensing-in
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will build on research that detects different types of uncertainty in deep neural networks, and we will connect this uncertainty to interactive data collection, e.g. in the form of a dialogue with the
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communications see our review paper entitled “Semantic Communications in Networked Systems: A Data Significance Perspective” published in IEEE Network, vol. 36, July/August 2022. The project is part of a
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of WWSC is to offer sustainable bio-based alternatives to current fossil-based materials. As a PhD member of WWSC, you will have access to a large research network across major universities in Sweden (KTH
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a PhD member of WWSC, you will have access to a large research network across major universities in Sweden (KTH, Chalmers and Linköping University). In addition, you will become part of the WWSC
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media and internet infrastructure computing cultures and materialities as heritage values and economies in algorithmic/data cultures social and cultural perspectives on dismantling communication networks