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relevant to modern data science (e.g., Bayesian or frequentist inference, information theory, uncertainty quantification, high-dimensional methods). Programming skills in Python and/or R, with evidence of
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expertise in areas such as approximate inference, Bayesian statistics, continuous optimization, information geometry, etc. We work on a variety of learning problems, especially those involving supervised
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specimens to estimate historical age structures over the last 150 years. Forecasting Shifts in the Pollination Service Window. The researcher will use Bayesian inference (e.g., Integrated Nested Laplace
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field (e.g., geography, resource management, environmental studies/science, or related disciplines) with strong experience in causal inference research. The ideal candidate will be a highly motivated
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experience in one or more of: large-scale data analysis, time-series photometry, spectroscopy, astrometry, Bayesian/statistical inference, and/or software development for astronomical datasets. Department
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/bayesian/deep-learning analyses, with functional validation in spruce via CRISPR-Cas9 and nanoparticle delivery. The postdoc will join Professor Nathaniel R. Street’s team at UPSC, working closely with
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of the following topics will be appreciated, but mostly we look for smart people who enjoy learning new things: Approximate Bayesian inference Differential geometry Numerical computations (ideally with experience in
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computational analyses, as well as statistical inference, for models describing the proliferation, mutation, and selection of blood cell precursors in human bone marrow. A primary focus will be advancing
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 3 months ago
fields including health, agriculture and ecology, sustainable development. More information, please visit https://team.inria.fr/scool/projects Odalric-Ambrym Maillard is a permanent researcher at Inria. He
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at the intersection of systems neuroscience and computational modeling. Our lab is broadly interested in Bayesian inference, perception, multisensory integration, spatial navigation, sensorimotor loops, embodied