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, extremal combinatorics, structural graph theory, and related fields. Qualifications and personal qualities Applicants must hold a master's degree or equivalent education in Mathematics (Combinatorics and/or
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-based networks graph-based approaches Bayesian learning information theory Documented strong programming skills (preferably Python), for example with contributions to open-source projects, with an active
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topics such as: neural networks self-supervised learning convolutional neural networks transformer-based networks graph-based approaches Bayesian learning information theory Documented strong programming
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to: Investigate how logical and philosophical theories can support the structuring and modelling of knowledge in practical contexts. Participate in the development of IMF by bringing in methods from formal ontology