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provably powerful learning models for graphs will require new mathematical machinery. LOGSMS will combine diverse tools from discrete mathematics, learning theory and machine learning, thus facilitating
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English. A strong background in graph theory and graph algorithms is necessary. For PhD position 1, we appreciate prior mathematical exposure to at least one of the following topics: random graphs
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, which arise from processes such as hybridization, horizontal gene transfer, and recombination. Creating such networks from DNA sequences requires techniques from graph theory, theoretical computer science
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