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including: * Algorithmic game theory * Approximation algorithms * Automata and formal languages * Combinatorics and graph algorithms * Computational complexity * Logic and games * Online and dynamic
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probability, allow for the application of tools from probability theory to combinatorial problems and motivate the study of the typical properties of various combinatorial models, such as the Erdős–Rényi random
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. You will: Develop and apply novel graph learning based methods for predicting the synthesisability of organic molecules Develop and apply novel neurosymbolic AI methods to chemical problems If appointed
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they will be proficient at coding. They will be passionate about creating new theorems and theories in mathematics and physics. We especially welcome academic researchers who are looking for a new career
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: Development and application of novel graph learning based methods Development and application of novel AI methods for predicting the synthesisability of organic molecules If appointed at a higher spine point
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robust feedback control theory for nonlinear dynamical systems using input-output operator theory and graph separation theory. Excellent knowledge of input-output dissipativity theory, input-output
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algorithmic graph theory. The purpose of the role is to contribute to the project "Algorithmic meta-classifications for graph containment", working with Professor Matthew Johnson, Dr Barnaby Martin and
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The Role Applications are invited for a Postdoctoral Research Associate in Computer Science with a particular emphasis on structural and algorithmic graph theory. The purpose of the role is to
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that uncover spatial organisation and predict dynamic behaviours in complex tissue systems. Drawing on ideas from ecology and network theory, you will build new tools to model spatial biological processes
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of focus include random graphs and trees (combinatorial properties, scaling limits, coalescent and branching structures, random matrix theory), stochastic and Lévy processes in infinite-dimensional spaces