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learning, small data learning · Active learning, Bayesian deep learning, uncertainty quantification · Graph neural networks This position involves active participation in a well-funded
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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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contribute to the development of a proof of concept obtained at University Côte d’Azur for accessing the content of a metabolomics knowledge graph (KG) with a large language model. It is Python prototype of a
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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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Michael Bronstein, AITHYRA Scientific Director AI and Honorary Professor of the Technical University of Vienna in collaboration with Ismail Ilkan Ceylan, expert in graph machine learning, invites
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: Design and implement AI/ML pipelines for multi-omics data integration, including supervised and unsupervised learning methods. Develop deep learning architectures (e.g., variational autoencoders, graph
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. Mathematically, a network is represented by a graph, which is a collection of nodes that are connected to each other by edges. The nodes represent the objects of the network and the edges represent relationships
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ideal candidate has strong familiarity with several of the following subjects: algorithmics, mathematical modelling, graph transformation, algorithm engineering. Applications of these areas to systems
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with collaborative research methods, contributing to the lab’s graph-based notetaking and knowledge base. • Explore innovative research dissemination methods, including micropublishing, iterative
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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