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transforming into a hub for global leaders to develop and promote human-centric technology and social policies. Further information about Lingnan University is available at https://www.ln.edu.hk/ . Applications
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. Research areas include Representation Learning, Machine learning and Optimization on graphs and manifolds, as well as applications of geometric methods in the Sciences. This is a one-year position with
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. This scholarship aims to support PhD-level studies on FAE by exploiting a new methodology that combines deep learning technology and knowledge graphs. The goal is to research and develop a new Decision Support
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Matter Physics o Physical Chemistry and Theoretical Chemistry o Combinatorics, Algorithm, Extremal Graph Theory, Computing Theory o Programming Language, AI Theory or Machine Learning o Classical and
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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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well as interact with other members of the algorithms research group. We are looking for excellent candidates with a background and experience in one or more of the following areas: graph algorithms, parameterized
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in those complex systems and related concepts. As part of this study, the researcher wilt be required to develop Python freeware to analyze and visualize graphs aiming towards explainability of dynamic
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. Research areas include Representation Learning, Machine learning and Optimization on graphs and manifolds, as well as applications of geometric methods in the Sciences. This is a one-year position with
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statistical physics. Specific Requirements Preference will be given to candidates with experience in research related to machine learning, graph theory, statistical physics, and modeling of stochastic systems
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: • Graph-based learning and community detection: Identify cohesive and antagonistic groups within signed networks. • Machine learning and network embeddings: Measure consensus, polarity, and opinion shifts