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Field
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(PINNs) and surrogate modelling Time-series modelling and anomaly detection Bayesian methods and uncertainty quantification Graph Neural Networks (GNNs) Spatiotemporal data engineering Digital twins and
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Qualifications Expertise and interests in artificial intelligence, machine learning, big data and network analysis, computational and Bayesian methods. Experience, desire, and flexibility to teach a range of
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, mixed-effects modeling, Bayesian methods, deep learning, variational autoencoders, generative AI). Is an experienced programmer in R and/or Python, and used to working with large datasets and reproducible
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studies maintained by the Institute, harmonised data from the SPI-Birds Network & Database, and other major biodiversity monitoring and data standardisation schemes—you will assess the generality
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areas will be considered when selecting candidates: Machine Learning, Neural Networks, Numerical solutions of Partial Differential Equations and Stochastic Differential Equations, Numerical Optimization
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entitled “Beyond Data-Augmentation: Advancing Bayesian Inference for Stochastic Disease Transmission Models”. The overarching aim of the project is to develop the next generation of statistical tools
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of the M.H. Mohseni Institute of Urologic Sciences providing access to cutting-edge facilities and collaborative research networks, opportunities for leadership in translational science, and a vibrant academic
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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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, or multitrophic interactions is a strong merit. Experience with computational methods such as multilayer networks, Bayesian inference, or higher-order network models is also a merit. The ability to communicate
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in AI (machine learning, neural networks, reinforcement learning, dynamic modelling and/or Bayesian inference). You have experience in software development with solid knowledge of one or more