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(PGMs) and graph neural networks (GNNs) to enhance Bayesian receiver design and beamforming in multiuser THz MIMO systems. By combining the complementary strengths of PGMs and GNNs in modeling relational
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Energy Storage Technologies RESTORATIVE is a pioneering Marie Skłodowska-Curie Actions (MSCA) Doctoral Network dedicated to accelerating the green transition through Thermo-Mechanical Grid-Scale Energy
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networks for real-time, adaptive diagnosis. b) Uncertainty in Dynamic Environments: Runtime uncertainties require sophisticated risk modeling; we will employ Bayesian deep learning and deep reinforcement
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within the SecReSy4You MSCA Doctoral Network at Eindhoven University of Technology. Information The Dynamics and Control group at Eindhoven University of Technology (TU/e) conducts world-class research
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: Bayesian hierarchical deconvolution of spatial bins using matched snRNA-seq reference, cell-cell communication inference, and spatial niche identification Multi-omics integration: linking spatial and single
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or more of the following: ecological modelling, dynamical systems, network analysis, Bayesian statistics or probabilistic modelling, mathematical biology, multivariate data analysis. Interest in connecting
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) relationship with the low-fidelity response. Extensions include nonlinear information fusion with GPs, Bayesian multi-fidelity inference and deep probabilistic surrogates, as well as MF neural networks
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, i.e. interconnected ecosystems). Recent developments have indeed sought to establish the link between scales using Bayesian dynamic networks (Trifonova et al. 2025). This article proposes a strategy
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Sklodowska-Curie Doctoral Network linking 21 academic, cultural, and industrial partners to develop advanced nondestructive evaluation and data-driven digital tools for paintings and 3D artworks (https
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Bayesian optimization and other active learning techniques to guide experimental efforts by identifying optimal chemical compositions and processing conditions of membranes that maximize both selectivity and