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description The position is connected to the project “Bayesian Enhanced Tensor Factorization Embedding Structure (BETTER)”, and this PhD project specifically aims at developing a unified, scalable, and
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equalizer (DFE) and a channel decoder based on PGMs and BP. The proposed research project aims to explore when and how combinedGNNs and PGMs can improve Bayesian receiver design and beamforming for multiuser
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connected to the project “Bayesian Enhanced Tensor Factorization Embedding Structure (BETTER)”, and this PhD project specifically aims at developing a unified, scalable, and interpretable framework for tensor
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environmental factors such as fluctuating wind speeds and saltwater exposure. Using advanced statistical and machine learning techniques, including Bayesian inference and stochastic modelling, the project will
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statistical and machine learning techniques, including Bayesian inference and stochastic modelling, the project will quantify and analyse uncertainties in the design and operational performance
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experience. This PhD project investigates how such radio signal cues can be identified, selected, and integrated into learning-based navigation frameworks. Research Objectives: The PhD candidate will
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-objective, real-time) and supply-chain optimization; PdM and RUL with health monitoring; digital twins/smart factories, cross-site transfer and federated/edge learning; uncertainty estimation and calibration
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analysis, Bayesian Skyline Plots, PCA, Bayescan - information provided in the CV and/or in the motivation letter; Other professional experience: teaching activities in evolutionary biology and phylogenetics