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Quantum computing and Graph theory In this role, you will be responsible for conducting research on graph theoretic approaches to design quantum photonic experiments. Additionally, the position involves
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science, computational chemistry / biochemistry, applied mathematics, or a related area. The ideal candidate has strong familiarity with several of the following subjects: algorithmics, mathematical modelling, graph
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this interdisciplinary project, we are looking for a strong candidate to contribute to the development of quantum algorithms and applications, focusing on quantum walks and quantum machine learning on graph structures
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independently. Must be able to organize and manage a varied range of assignments and projects with high efficiency. Able to work with database, graphing, word processing, and statistical computer programs such as
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causal inference methods (e.g., directed acyclic graphs) would be an asset Fluent in reading, writing, and speaking scientific English Required Skills : Experience in data management and analysis of cohort
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Applications are invited for a 2-years position as postdoctoral researcher within the research project “Graphs and Ontologies for Literary Evolution Models” (GOLEM), financed by an ERC Starting
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modelling and simulation of complex systems reinforcement learning or graph neural networks proficiency in Python and related computational toolchains a strong interest in interdisciplinary research bridging
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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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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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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 9 hours ago
knowledge graphs. Your work will support the creation of FAIR-aligned metadata (including emerging standards like Croissant) to ensure data provenance, accessibility, and reuse across translational science