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Mathematical insight is arguably humanity’s greatest creative achievement—and one of its most resistant to automation. Unlike text prediction or image recognition, mathematics has seen only modest
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the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real
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: extremal graph theory, Ramsey theory, probabilistic combinatorics. • Candidates should have (or be near completion of) a PhD in mathematics. • Candidates should have a strong research record in Combinatorics
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graph theory, complex dynamical systems, computational mathematics, data science, difference equations, financial mathematics, mathematical biology and modeling, numerical analysis, numerical linear
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has a strong PhD program in Applied Mathematics, Applied Statistics, Graph Theory/Combinatorics, and Analysis, featuring active research collaborations both within the University and with external
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and Boulton, 1968; Wallace and Dowe, 1999a; Wallace, 2005) is a Bayesian information-theoretic principle in machine learning, statistics and data science. MML can be thought of in different ways - it
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questions within the theoretical and fundamental aspects of Mathematics, Physics, and Computer Science and Engineering, from various points of view and different abstraction levels. The selected candidate
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programs. Alternatively, Mathematics, Computer Science, Computer Engineering, Electrical Engineering, or a similar field; Strong mathematical background: basic knowledge of graph theory and excellent
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a similar field; Strong mathematical background: basic knowledge of graph theory and excellent background in linear algebra, finite fields and rings; Strong background in digital hardware design and
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, or a similar field; Strong mathematical background: basic knowledge of graph theory and excellent background in linear algebra, finite fields and rings; Strong background in digital hardware design and