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Are you interested in advanced computational modelling and state-of-the-art scientific software? Join us in creating the Bayesian multiverse: a computational framework for robust statistical analyses
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reinforcement learning (RL), active learning, Bayesian decision theory, and stochastic optimisation for partially observed and evolving systems. Key research directions include: adaptive data acquisition
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integration, especially in molecular medicine; mathematical modelling of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; causal inference and time
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enjoys a wide network of strong international collaborators all around the world, for example at the University of Oxford, the University of Melbourne, and the University of California, Los Angeles. We
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mathematical background Core skills: Probability and statistics. Estimation, Bayesian inference, uncertainty quantification and calibration (proper scoring rules, reliability diagrams, ECE), experiment design
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data and data integration, especially in molecular medicine; mathematical modelling of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; causal
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research projects at the intersection of infectious disease modelling, Bayesian inference, AI, and public health. Projects span AI-driven epidemic forecasting, transmission modelling in the ASEAN region
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Inria, the French national research institute for the digital sciences | Saclay, le de France | France | 3 months ago
modeled using an evolution of this algorithm, which replaces the SVRs with a multilayer neural network [Ribes et al., 2024]. In 2025, it was used in an inverse-computation context using Bayesian inversion
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. Application of Bayesian mixing models to investigate consumer diets and food web pathways Requirements To meet the general entry requirements you must have been awarded a second-cycle (Master’s) qualification
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with researchers and practitioners through the aiD network and related national initiatives. Contribute to the scientific development and collaborative activities of the Data Science research group