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Field
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is promising, but delivers very local measurement only. Therefore, it would be interesting to combine these local measurements with distributed measurements of fluxes that can reveal variations in
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lack a direct correlation with process parameters, limiting their ability to predict temperature fields under varying process conditions. The transferred arc energy distribution becomes particularly
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to work in. Research groups: Computational Neurosciences Computer Graphics and Ecological Informatics Computer Networks Computer Security Databases and Information Systems Data Fusion Data Science
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methods for distributed state estimation in large and heterogeneous sensor networks, with particular emphasis on statistical model‑based approaches. As sensor systems scale up to vehicle fleets, drone
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is optimally placed to improve the overall defence performance of a distributed counter-drone capability. The use of a multistatic radar network can potentially provide a significant improvement in
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time delays need to be compensated [6]. Accurate channel estimation is known to be a difficult task in UM-MIMO settings [7,8] as it usually relies only on pilot symbols to estimate a large number of
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Engineering and will become part of the national graduate school FOFOS – Research School for the Transformation of the Public Sector (https://www.mdu.se/forskning/forskarskolor/forskarskolan-fofos ). FOFOS is
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 21 days ago
solving complex inverse problems that link measurements to their underlying causes. This PhD interdisciplinary programme focuses on Bayesian methods for estimating physical parameters from high-dimensional
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for the future of mobile and satellite communications. Fields of applications range from 5G/6G telecommunications to satellite-based internet connectivity. For details, you may refer to the following: https
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currently lack reliable uncertainty estimates, limiting error detection and automation. The UMLFF project aims to develop next-generation MLFFs with built-in uncertainty predictions to enable safe, automated