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Field
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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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via nonlinear parametrizations such as deep networks, dynamical systems and control, Bayesian inference and generative modeling, and randomized linear algebra. Applications of interest are transport
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Experiments (DoE) and Bayesian optimization, manage research data using the NOMAD research data infrastructure, and apply data-driven optimization strategies. Analyze and interpret experimental data
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to contribute to developing new ones Fluency and experience with high performance and/or high throughput computing Expertise in the theory and usage of high-level Bayesian and/or frequentist statistical data
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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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statistical analysis including multivariate methods, data reduction techniques, structural equation modeling, survival analysis and Bayesian hierarchical imaging models, graph neural networks. The specialist
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expression systems and/or liposomes. You take an interest in liquid handling robots, Python and Bayesian optimisation. You are a team player and enjoy working in a multidisciplinary environment. You have a
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will include developing flexible Bayesian approaches to investigate how circadian rhythm disruption (CRD) affects cell composition of a tissue by using spatial clustering algorithms to the spatial
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and neuro-cognitive methods). • You collaborate and support other team members in their advanced data analyses, with a focus on computational modeling and Bayesian analysis approaches. • You teach
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data science approaches includes the application of Bayesian inference or probabilistic machine learning to geophysical models. UiO is subject to the Security Act, which governs the organisation's