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to Fisher-Rao geometry and develop theoretical results characterising MML estimation under various regularity conditions. Aim 2: Development of Computational Methods for MML Design and implement efficient
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geometry, and/or data science. Specific topics of focus include, but are not limited to, linear response, random and nonautonomous dynamical systems, spectral analysis, machine learning, data-driven dynamics
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Research Fellows who have: a Ph.D. in mathematics with a research background in an area of computational pure mathematics (such as algebra, arithmetic geometry, number theory, group theory, representation
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research background in an area of computational pure mathematics (such as algebra, arithmetic geometry, number theory, group theory, representation theory, commutative algebra, or algebraic geometry
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and education in the School of Computer and Mathematical Sciences. The successful candidate will be an outstanding researcher in pure mathematics with strong plans for future research and broad
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the School of Computer and Mathematical Sciences. The successful candidate will be an outstanding researcher in pure mathematics with strong plans for future research and broad experience in teaching and
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would suit a mathematics or computer science student with a background in continuous optimisation (linear and non-linear programming) and discrete optimisation (mixed integer programming). An ability