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Field
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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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maximum likelihood and Bayesian inference frameworks. - Data mining in genome databases. - Large-scale phylogeny reconstruction (archaea, bacteria, and eukaryotes). - Implementation of complex sequence
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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 | 22 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 | 16 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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diagnosis of psychosis. The postdoctoral researcher will lead a research program focused on developing and testing the computational mechanisms of social inference, although will have plenty of scope, and
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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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Australian National University | Canberra, Australian Capital Territory | Australia | about 2 months ago
, approximate inference, deep learning, or Bayesian optimisation are encouraged to apply. Interpretable Machine Learning for Natural Language – Led by Prof Lexing Xie, this stream applies machine learning
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will be grounded in rigorous mathematics coupled with a sound understanding of the underlying earthworm ecology. Bayesian inference methodologies will be developed to estimate where and when behavioural
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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