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infection. For more information, please see https://www.scilifelab.se/data-driven/ddls-research-school/ The future of life science is data-driven. Will you be part of that change? Then join us in this unique
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techniques including graph neural networks, Bayesian neural networks, conformal prediction intervals and generative AI for synthetic data generation. You will also develop frameworks for uncertainty
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dimensionality reduction and Bayesian multilevel modeling. The project provides access to a rich, curated longitudinal clinical dataset and close collaboration with leading neurologists, neurosurgeons, and data
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research will focus on developing Bayesian Optimization of Experimental Design (BOED) algorithms. These algorithms aim to cost-effectively accelerate reliability tests for TM-GSES components, maximizing
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manufacturing. The role sits within the £13M EPSRC Hub for Advanced Long-Acting Therapeutics (HALo) (https://www.liverpool.ac.uk/celt-global-health/halo/), a nationally leading initiative spanning universities in
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and propagate uncertainty from image features to predicted PEMWE behavior. Bayesian experimental design and process optimization. The digital twin will form the basis for Bayesian optimization
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Website https://emploi.cnrs.fr/Offres/CDD/UMR7058-THOKIC-002/Default.aspx Requirements Research FieldGeosciencesEducation LevelPhD or equivalent Research FieldBiological sciencesEducation LevelPhD
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generative models, methods for approximate inference, probabilistic programming, Bayesian deep learning, causal inference, reinforcement learning, graph neural networks, and geometric deep learning. Want
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of Euclid data. Where to apply Website https://emploi.cnrs.fr/Offres/CDD/UMR8262-ELIBLA-002/Default.aspx Requirements Research FieldAstronomyEducation LevelPhD or equivalent LanguagesFRENCHLevelBasic Research
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or more of the following: ecological modelling, dynamical systems, network analysis, Bayesian statistics or probabilistic modelling, mathematical biology, multivariate data analysis. Interest in connecting