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Field
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learning, AI interpretability, formal verification methods, modeling emergent risks from AI R&D automation. Candidates are encouraged to interpret these subject areas broadly. Fellows will have access
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organism movement and behavior through image tracking Advancing unsupervised and semi-supervised classification methods to discover previously unrecognized biological groupings Explore adaptive AI-guided
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interested in applicants who use advanced quantitative methods, including computational modeling, machine learning, and/or analyzing structural and functional neuroimaging data. Specific activities may include
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/index.html The researcher will be part of a growing team of researchers, postdocs and PhD students working on intelligent observing systems using machine learning and data assimilation methods in the ACTIVATE