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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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drone detection and localisation performance using radar systems. This can be achieved by improving the detection performance of individual sensors and by employing a cooperative network of sensors which
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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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- Markovian Processes - Network Based Localisation / Radio based connectivity - Adaptive bandwidth - Mesh networking - Wireless Sensor Networks - Edge Computing - Time Delayed Systems - Adaptive Queuing
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information, they are computationally intensive, which prevents their systematic use in large parametric studies. Consequently, simplified or surrogate models (e.g., 1D network models, reduced-order models
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(GPR), complemented by soil sensors and borehole data. A particular emphasis will be placed on the combined use of borehole and surface GPR, as well as small-scale EMI measurements in controlled
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fundamental and applied research within communication, networks, control systems, AI, sound, cyber security, and robotics. The department plays an active role in transferring inventions and results
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Research Infrastructure? No Offer Description MetaTune is a Marie Sklodowska-Curie Doctoral Network project, funded by the European Commission, Horizon Europe Program, starting in March 2026. The Network
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and spatially complex nature of MRI signals. Each MRI examination involves multiple pulse sequences, with signal acquisition being sensitive to coil placement, sensor geometry, B0/B1 inhomogeneities
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net radiation, and because snowmelt and evapotranspiration (ET) of meltwater are sinks for latent heat. Growing season water inputs in these environments originate from both rain and meltwater, but