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for a/an University assistant predoctoral - PhD Position in Graph Learning 39 Faculty of Computer Science Startdate: 01.05.2026 | Working hours: 30 | Collective bargaining agreement: §48 VwGr. B1
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Description Are you excited about using large-scale AI to accelerate scientific discovery? Join a Horizon Europe project developing next-generation scientific foundation models that combine knowledge graphs
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on Duke Academy, our mastery‑based learning platform that blends AI‑powered tutoring, knowledge‑graph‑driven pathways, and interactive browser‑based coding. Every day, you’ll contribute to a product that is
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to work effectively in an interdisciplinary team. PREFERRED QUALIFICATIONS Experience with one or more of the following: knowledge graphs, graph machine learning, link prediction, representation learning
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | about 4 hours ago
the replicator equation. The candidate(s) may also be required to develop computational and algorithmic platforms to link models to biological data. The project integrates dynamical systems, graph theory, linear
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France 91120, France [map ] Subject Areas: Applied Mathematics - statistical learning, graph learning or large language models Appl Deadline: 2026/03/24 03:59 AM UnitedKingdomTime (posted 2026/02/03 05:00
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[6]. (iv) Le quatrième vise la gestion de la convergence et de l’équité des modèles asynchrones en utilisant les graphes pour modéliser et garantir des politiques égalitaires ou équitables. La présence
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and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities
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Applied to Neuroscience and Drug Discovery Where to apply Website https://gestiononline.bioef.eus/ConvocatoriasPropiasBiobizkaia/es/Convocatorias… Requirements Research FieldOtherEducation LevelPhD
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advancement of the research of deep neural networks, in the field of adaptive processing of graph data (Deep Graph Learning). The project includes the following strongly interconnected fundamental research