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relevant expertise: A PhD in Computer Science or a closely related field, with specialization in Quantum computing and Graph theory In this role, you will be responsible for conducting research on graph
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analysis, combinatorics, computational mathematics, differential equations, dynamical systems, geometry, graph theory, mathematical biology, mathematics education, number theory, probability, and statistics
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) Experience with modern deep learning frameworks (e.g., PyTorch, JAX, TensorFlow) Background in at least one of the following: graph learning, scientific computing, surrogate modeling, or ML theory Interest in
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: Statistical analysis & causal inference Data management, collection & visualization Social network analysis & graph theory Online tool/web development Running controlled experiments Game-theoretic modelling
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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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/ Knowledge Graph Representation / Recommender Systems Graph Theory/Network Science Python, and up-to-date machine learning libraries Excellent written and verbal communication skills Track record of publishing
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associate to work on one or more of the following topics: Mathematical Physics, Spectral Theory, Quantum Chaos, Large Graphs and Quantum Walks. Related areas such as Quantum Information can also be considered
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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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are passionate about any or all of the following: Data Science, Computational Social Science, Behavioral Economics, Human-Bot interaction, Experimental Research, Game Theory, and Artificial Intelligence. Some of
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, graph theory, graph-signal processing, and convex/non-convex optimization. Furthermore, our activities are experimentally driven and supported by the COMMLab , the 6GSPACE Lab , the HybridNetLab