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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | about 5 hours ago
macromolecular dynamics, as well as collaborations with the European MDDB initiative. The candidate will develop new graph transformer architectures to learn conformational heterogeneity from molecular dynamics
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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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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | about 5 hours ago
of the proposed research subject : A state of the art, bibliography and scientific references are available at the following URL: https://vincentgaudilliere.github.io/files
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yield new insights into food-effector systems, sophisticated and tailored computational methods are needed. This project aims at leveraging graph-theoretic approaches to analyze and predict food-effector
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, learner-aware sequencing of content. This includes work on semantic parsing, structured NLP, graph-based neural models, metacognitive prompting, ontology alignment across disciplines, and human-in-the-loop
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superstructure combining cycles from the literature and cycles generated by AI models. -- Use of process representation formalisms (graphs, SFILES) and process synthesis tools. - Solving the “Product Design
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investigate deep learning architectures capable of learning microstructure-property mappings, including convolutional neural networks for microstructure image analysis, graph-based representations
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education to enable regions to expand quickly and sustainably. In fact, the future is made here. Umeå University is offering a PhD position in Computing Science with a focus on machine learning for graph
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the form of graphs to analyze and predict food-effector systems. Key Responsibilities Develop Probabilistic Machine Learning Models to integrate graphs and food-related omics data Multi-omics integration
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investigate deep learning architectures capable of learning microstructure-property mappings, including convolutional neural networks for microstructure image analysis, graph-based representations