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Field
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there are innumerable examples of its application, one important observation is the low proportion of studies proposing the estimation of uncertainties (<5%). Yet uncertainties can be multiple and of different natures
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, so that it can be easily used in practice (fast optimization, embedded decision-making, online updating). 1. Design a lightweight statistical/probabilistic surrogate model, integrating: • an estimation
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-dimensional niche models, and applying advanced Bayesian spatio-temporal methods. You will: Build n-dimensional abiotic niches for >6,700 species and estimate population positions within them. Quantify niche
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Climate Plan. You will research, use and build on existing methods to take data about the subsurface (seismic surveys, borehole data, geological mapping and other data) and produce estimates of the physical
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Bayesian machine learning for robotics applications in industrial manufacturing and medical diagnosis, SDU Robotics invites applications for one or more fully funded Research Assistant positions
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Inria, the French national research institute for the digital sciences | Saint Martin, Midi Pyrenees | France | 14 days ago
specifically, we use simulation-based inference (SBI) [1], a Bayesian approach that leverages deep generative models, such as conditional normalizing flows and score-diffusion models, to approximate
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. The work will apply state-of-the-art three-dimensional atmospheric chemistry and circulation models, together with advanced statistical techniques (optimal Bayesian, Markov Chain-MonteCarlo, etc.) to solve
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, clustering analyses, propagating location and other uncertainties...) of mid-ocean ridge catalogs, using standard, Bayesian and machine learning techniques. ⁃ Implement methodologies that improve estimates
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theories from tractable models (probabilistic circuits) and Bayesian statistics to tackle the reliability of machine learning models, touching topics such as uncertainty quantification in large-scale models
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on proving conditions under which such algorithms are optimal, and develop mathematical bounds on their sub-optimality in more complex cases. 3) Numerical Solutions to Bayesian Optimal Stopping Problems