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Inria, the French national research institute for the digital sciences | Saint Martin, Midi Pyrenees | France | 13 days ago
: surrogates, neural operators, active learning, online training, Bayesian methods. Then -- start to work on possible generative methods for active learing (normalizing flows, diffusion models, generative
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). -Interest in Bayesian inference. - Knowledge of non-Gaussian models (heavy-tailed, impulsive) is an asset. Additional Information Work Location(s) Number of offers available1Company/InstituteUniversité
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 1 hour ago
are particularly interested in scholars who advance methodological frontiers, such as causal inference, complex systems modeling, implementation science, longitudinal or big-data analytics, community-engaged methods
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datasets. Apply sensitivity analysis, parameter subset selection, and Bayesian inference to improve model identifiability and predictive capability. Implement computational pipelines in Python, MATLAB, and
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service. We welcome applicants from all areas of statistics. Preference will be given to candidates whose research interests overlap with the existing faculty, particularly causal inference, high
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analysis Experience in one or more of the following areas is highly desirable: Hybrid modelling, neural differential equations, or physics-informed neural networks Equation discovery Bayesian inference
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 21 days ago
of Biostatistics. Specifically, the position works on and provides oversight to several federal and industry research and training grants in the areas of casual inference, Bayesian methods, robust methods, frailty
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 14 days ago
of Biostatistics. Specifically, the position works on and provides oversight to several federal and industry research and training grants in the areas of casual inference, Bayesian methods, robust methods, frailty
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. We are interested in candidates with research interests in causal inference or Bayesian methodology, and we also welcome strong applicants from the broader fields of statistics and machine learning
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the eDIAMOND project, namely: Distributing model training and inference over a network of resource-constrained devices. Online, context-aware adaptation of Federated Neural Network Architectures based