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interactions, and will emphasize rigorous uncertainty quantification and reproducible, scalable analysis of large multi-omics datasets. The successful candidate will be expected to work closely with clinical and
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. · Quantification and Propagation of uncertainty in industrial environments (noisy sensors, sensor degradation, evolving production processes, rare events, incomplete datasets…) o quantifying epistemic and
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-the-loop, decision-making for complex systems, optimisation for LLMs, foundation models, dimensionality reduction, deep learning, uncertainty quantification, language, and developmental robotics. About You
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to high-dimensional statistics; Bayesian statistics; resampling techniques; digital twins; uncertainty quantification; foundations of machine learning and artificial intelligence; optimization theory and
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, recurrent memory, Bayesian modelling, uncertainty quantification and machine learning systems. Emphasis will be on methods that design and implement new architectures for (auto-regressive) sequence modelling
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Description The main tasks to be carried out by the selected candidate will be the following: ● Develop new methods for the uncertainty quantification of non-linear statistical models using the 'online
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simplification. Particular attention will be paid to uncertainty quantification and to the definition of criteria that assess the contribution of each simulation to the improvement of the prediction
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of Bayesian approaches such as Gaussian process regression, particle filters, Bayesian networks, graph-based approaches. Probabilistic -based uncertainty quantification is also essential. Support the design
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of computational and applied mathematics, including but not limited to data-driven numerical modeling, scientific machine learning and AI for science and engineering, computational uncertainty quantification
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connection with uncertainty or data science. Specific fields of interest include, but are not limited to, stochastic partial differential equations, optimal transport, gradient flows, uncertainty