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ongoing research on mechanistic modelling and Bayesian parameter inference from in vitro neural models. Combine theory, simulation, and data-driven methods, this in close interaction with the researchers
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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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Bayesian inference, probabilistic modeling, and machine learning, the project aims to make Arctic observations more efficient, intelligent, and impactful. You will integrate field observations—including
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is required Desired qualifications: Experience with data assimilation, probabilistic machine learning, Bayesian inference, inverse modeling, and/or simulation-based inference is an advantage
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polycrystalline material during plastic deformation in order to eventually predict the manner in which materials deform and fail. As a first step, we wish to infer a distribution of the directions of deformation
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medicine and diagnostics, epidemiology and biology of infection. For more information, please see https://www.scilifelab.se/data-driven/ddls-research-school/ The future of life science is data-driven. Will
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criteria Machine Learning Expertise: A robust foundation in probabilistic modeling, Bayesian inference, deep learning, and/or anomaly detection Modeling & Simulation Experience: Familiarity with Building
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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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data, epidemiologic modeling, Bayesian analysis, modern causal inference, statistical genetics and genomics, machine learning methods, health economics, survey design, systematic reviews, behavioral
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requirements, is provided below and in the links. Project descriptions Project 1: Predictive Bayesian inference and foundation models Employment: University of Oslo, Department of Mathematics PhD programme