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plants they visit and pollinate. Bayesian networks (BNs), and other probabilistic graphical models, can provide a visual representation of the underlying structure of a complex system by representing
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. Yet, many stellar and planetary parameters remain systematically uncertain due to limitations in stellar modelling and data interpretation. This PhD project will develop Bayesian Hierarchical Models
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University of Massachusetts Medical School | Worcester, Massachusetts | United States | 38 minutes ago
datasets to dissect cell states and communication networks in diseased and healthy tissues. Genetic and longitudinal modeling, integrating genomic variation with real-world longitudinal data—including
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Investigate the use of causal discovery methods in "concept drift" situations in structural causal models. In semiparametric Bayesian networks, investigate the selection of covariance matrices and the
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Description Distribution estimation algorithms for abductive inference (total or partial) in dynamic domains. Structural learning of dynamic Bayesian networks with discrete and continuous variables (parametric
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This PhD project is funded by a successful ARC Discovery Project grant: "Improving human reasoning with causal Bayesian networks: a user-centric, multimodal, interactive approach" and the successful
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Research Engineer/Postdoctoral Position Decision and Bayesian Computation (DBC) – Epiméthée (EPI) Laboratory Institut Pasteur, Paris | 25 rue du Docteur Roux, 75015 Paris Position Overview We
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resonators) and quantum (NV diamond) sensor networks for thermodynamic metrology (temperature, pressure and humidity). key words Machine Learning; Bayesian optimization; Self-correcting; Quantum Sensors; NV
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networks. You should have experience of building machine learning models for environmental applications. A high level of data science and computational expertise is essential, as is experience with Bayesian
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The role The Atmospheric Chemistry Research Group (ACRG) and School of Engineering Mathematics at the University of Bristol have developed GATES, a graph neural network (GNN) machine learning model