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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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expertise in controlling and deploying them in practice, as well as in designing coordination strategies for them. Our recent work on RL and graph neural networks (GNNs) demonstrate some of our key research
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and with expertise spanning knowledge graphs, GeoAI, research data management, geo-semantics, geographic information retrieval, geo-visualization, social sensing, and related fields. We are interested
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, or a related field, with a focus on spatial analysis, wayfinding, or evidence-based design, as well as: Expertise in space syntax analytic methods, including isovist analysis, justified graphs, and
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learning architectures including generative models, particularly for sequence or structural data (e.g. transformers, graph neural networks) Proved experience in working independently and as part of a
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structural and 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
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to develop a knowledge-aware and event-centric framework for natural language understanding, in which event graphs are built as reading progresses; event representations are learned with the incorporation
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for sequence or structural data (e.g. transformers, graph neural networks) Proved experience in working independently and as part of a multidisciplinary team Evidence of strong communication and scientific
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graph neural networks for complex sensor networks such as those involved in brain imaging Develop and test data-driven methods for image and video processing for microendoscopy. Key Duties and