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Field
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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
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. The ideal candidate will enhance our biostatistical core and complement or deepen our current department strengths, including, but not limited to: Bayesian methods, big data, causal inference, clinical trials
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. The ideal candidate will enhance our biostatistical core and complement or deepen our current department strengths, including, but not limited to: Bayesian methods, big data, causal inference, clinical trials
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or deepen our current department strengths, including, but not limited to: Bayesian methods, big data, causal inference, clinical trials, machine learning, mobile health data, real world evidence, survival
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or deepen our current department strengths, including, but not limited to: Bayesian methods, big data, causal inference, clinical trials, machine learning, mobile health data, real world evidence, survival
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or deepen our current department strengths, including, but not limited to: Bayesian methods, big data, causal inference, clinical trials, machine learning, mobile health data, real world evidence, survival