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, UAEs, GANs, diffusion models/flow matching, and Gaussian processes for realistic load, generation and voltage time series. You will embed physical constraints into generation: power-flow consistency
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identified by, a set of parameters. Such methods are a popular choice in quantum dynamics, where the wavefunctions of many-body systems are approximated by multiple Gaussians. The methodology of interest
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incorporate methods that integrate: - Mendelian randomization and genetic instruments - Bayesian hierarchical models and Gaussian graphical models - Multi-layer data integration across tissues and omics
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models/flow matching, and Gaussian processes for realistic load, generation and voltage time series. You will embed physical constraints into generation: power-flow consistency (Kirchhoff's laws) as soft
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Job related to staff position within a Research Infrastructure? No Offer Description Neural reconstruction pipelines (e.g. Gaussian Splatting) and 3D sensing technologies such as LiDAR and
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, UAEs, GANs, diffusion models/flow matching, and Gaussian processes for realistic load, generation and voltage time series. You will embed physical constraints into generation: power-flow consistency
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distribution, which lead to an extremal analogue of Gaussian structural causal models. We further propose to develop scalable structure learning methods for these new models, including latent variable
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, Bayesian inference, model calibration, and Markov Chain Monte Carlo methods, uncertainty quantification, statistical modelling, and Gaussian processes, machine learning for time series, sequence-to-sequence
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of settings where only parts of a system are extreme. Special attention will be given to parametric families like the Hüsler–Reiss distribution, which lead to an extremal analogue of Gaussian structural causal
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interactions to neutron-rich systems. The central aim of this project is to develop a Gaussian Process emulator for computationally expensive Relativistic Hartree-Bogoliubov and Quasiparticle Random Phase