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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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, 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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) 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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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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induced seismicity. Current models remain limited by the scarcity, heterogeneity, and noise of available data, as well as by incomplete knowledge of the subsurface. Physics-Informed Neural Networks (PINNs
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in English Candidates without a master’s degree have until 01.09.2026 to complete the final exam. Desired qualifications: Solid foundation in Bayesian statistics, empirical Bayes methods and advanced
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-informed / simulation-aware modeling Efficient algorithms for design-space exploration (e.g., surrogate modeling, Bayesian optimization, differentiable programming) Hybrid approaches combining data-driven
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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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01.09.2026 to complete the final exam. Desired qualifications: Solid foundation in Bayesian statistics, empirical Bayes methods and advanced probabilistic modelling Familiarity with stochastic processes (e.g
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. Bayesian networks and related machine-learning methods will be used to calculate cross-section probability density functions in a much faster way, enabling the combination of multiple probability