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funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The i3S laboratory (https
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graphs and network medicine • Translational data science for therapeutic discovery Primary Responsibilities: The Postdoctoral Research Associate is expected to lead and contribute to independent and
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of continual graph learning. Continual graph learning studies how graph neural networks can learn from a sequence of evolving tasks, graphs, or distributions while retaining previously acquired knowledges
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-agent systems b. Mapping and Scene Representations - Dynamic Scene Graphs modelling uncertainty - Unified situational awareness for multi-robot systems in large and degraded environments Qualifications
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | about 2 months ago
., Scharffe, F., Todorov, K., & Trojahn, C. (2025). Graph Embeddings Meet Link Keys Discovery for Entity Matching. https://doi.org/10.1145/3696410.3714581 [6] Sousa, G., Lima, R., & Trojahn, C. (2025). Results
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be responsible for Setup a knowledge graph in neo4J for microbiome research Integration of microbiome research data from the project with data from literature (e.g., molecular pathways) using
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; Build data and knowledge infrastructures, namely knowledge graphs, ontologies and multimodal representations of the CENSE scientific body; Collaborate with the CENSE team and external partners in
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, and EHR data. Experience with modern deep learning frameworks such as PyTorch, TensorFlow, Keras, Scikit-learn, Pandas, NumPy, and SciPy. Familiarity with convolutional neural networks (CNNs), graph
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Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. | Dortmund, Nordrhein Westfalen | Germany | about 2 months ago
for the Research Group Multidimensional Omics Data Analysis: PhD Candidate (m/f/d) You will be responsible for Setting up a knowledge graph in neo4J for microbiome research Integration of microbiome research data
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for analyzing how landscapes evolve over time. This thesis is part of the ANR GEvoK (Geographic Entities Evolution in Knowledge Graphs) project, which aims at automatically detecting and semantically