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Field
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include: Strong foundation in one of the following areas: Machine Learning / Information Retrieval / Knowledge Graph Representation / Recommender Systems Graph Theory/Network Science Python, and up-to-date
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the theory of quantum graph states. Additional expertise in computational methods would be useful but is not necessary. The Postdoctoral and Senior Research Associate positions will also involve
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graph theory. Qualifications Candidates with a Ph.D. in any area of cognitive neuroscience broadly defined (e.g., Psychology, Neuroscience, Computer Science, or a related field) are welcome to apply
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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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the WATOC Dirac Medal. As the successful candidate, you will contribute to the design and implementation of AI models that integrate quantum mechanical theory with deep learning, enabling rapid and
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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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generated from density functional theory simulations, we aim to interpret complex TERS images and push the limits of nanoscale optical imaging. The student will develop and implement new theoretical
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to: Investigate how logical and philosophical theories can support the structuring and modelling of knowledge in practical contexts. Participate in the development of IMF by bringing in methods from formal ontology
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quantum information theory, algebraic topology, polyhedral combinatorics, graph theory, and optimization are strongly encouraged to apply. For more details on the project, visit open positions page . To be
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., optimization, dynamical systems, graph theory, probabilistic modeling) or adjacent fields with engineering impact. (4) Fluency in prototyping and software development (e.g., Python, C++, CUDA, or ML frameworks