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Trustworthy Graph Machine Learning for Population Scale Networks Job description We invite applications for a postdoctoral researcher to work on fundamental techniques for trustworthy graph machine
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), ideally geometric/graph neural networks, equivariant models, or generative models. Interest in applying AI to molecular or biological problems; prior structural biology experience is a plus but not required
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learning, or decision-making for conversational agents; User modelling or computational modelling of engagement, curiosity, openness, or resistance; Knowledge graphs, ontologies, semantic technologies
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flows, flow matching, neural ODEs as well as graph neural networks. Looking forward, the aim is to develop new mathematical and computational paradigms that can deepen our scientific understanding of deep