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Graph Theory, Computing Theory o Programming Language, AI Theory or Machine Learning o Classical and Quantum Algorithm for Computational Quantum Many-body Theory o Theory and Computation of Correlated
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, control theory, data science, data driven methods, discrete mathematics, graph algorithms, high-performance computing, integral equations and nonlocal models, linear and multilinear algebra, machine
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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
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in the following areas: Deep Learning, Scientific Machine Learning, Stochastjc Gradiant Descent Method, and Numerical PDE’s - Advised by Dr. Yanzhao Cao Probabilistic Graph Theory (Network Traversal