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and optimization, we use tools such as artificial intelligence/machine learning, quantum conputing, graph theory, graph-signal processing, and convex/non-convex optimization. Furthermore, our activities
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use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities are experimentally driven and
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include statistical analysis, data management and collection, causal inference, network analysis, graph theory, visualizations, and online tool development. Experience in conducting online controlled
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to the large-scale nature, complexity, and heterogeneity of 6G networks, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal
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following areas: Probabilistic or Bayesian Machine Learning Variational Inference, Ensemble, or Diffusion Models Spatio-Temporal or Sequential Modelling Graph Neural Networks Deep Learning and Uncertainty
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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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) 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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are essential, particularly in one or more of the following areas: Probabilistic or Bayesian Machine Learning Variational Inference, Ensemble, or Diffusion Models Spatio-Temporal or Sequential Modelling Graph
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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