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. The successful candidate would work with an international network of researchers from the University of Adelaide, University of Tasmania, University of Melbourne, University of Copenhagen and the Norwegian
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from sensors or other continuous data sources. Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages
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will be adapted to the candidate’s background and the evolving needs of the center. Possible directions include the application of rock physics models, Bayesian inversion methods, and machine learning
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physics models, Bayesian inversion methods, and machine learning algorithms in the electromagnetic context. Qualifications and personal qualities: Applicants must hold a master’s degree (or equivalent) in
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), clinical trials, disease surveillance, and the use of novel methods including Bayesian network, machine learning, social network analysis and dynamic data visualisation tools. Further information is
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computational modeling, geometric morphometrics, multivariate and Bayesian statistics, spatiotemporal and spatial modeling (including GIS), causal inference, machine learning, AI, and statistical software
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Elhoseiny, Code: https://github.com/yli1/CLCL Uncertainty-guided Continual Learning with Bayesian Neural Networks (ICLR’20), Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, Marcus Rohrbach, Code: https
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Dalhousie University | Halifax Mid Harbour Nova Scotia Provincial Government, Nova Scotia | Canada | about 2 months ago
, and systems engineering. The advancement and application of techniques such as Systems-Theoretic Process Analysis (STPA), structured expert elicitation, and Bayesian Networks (BNs) are foreseen, and
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appropriate conditions, it provides a confidence set (credibility set if prediction is Bayesian) for a multivariate estimate with statistical coverage guarantees. This PhD project aims to develop new CP methods