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Field
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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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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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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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, 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
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-dimensional probability, random matrix theory, random graphs, stochastic analysis, and stochastic processes. Preference will be given to applicants with demonstrated research interests or expertise in
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Science, such as: Optimization, Machine Learning, Statistical Learning, Applied Probability, Graph Theory and Network Science, or Computational Statistics, with opportunities for cross-disciplinary
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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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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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) have some exposure to (hyper)graph theory, network science, and/or reaction mechanism/CRN studies. Candidates who do not meet all of these criteria should not feel discouraged. If you are interested in
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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