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processes. The excellent outcrop exposure due to limited vegetation cover, and extensive mineral occurrences provide a unique natural laboratory to advance predictive mineral system models. Our research will
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AI-supported predictive models for hydrothermal occurrence, and translate these into scientific outputs (peer-reviewed publications, informational maps, governance products). Further, contribution
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to improving the quality and efficiency of the consortium code base. Preference will be given to candidates with a strong publication record and proven experience in Python programming, source code versioning
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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