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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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administration and organisation. We are looking for a/an University assistant predoctoral - PhD Position in Graph Learning 39 Faculty of Computer Science Startdate: 01.05.2026 | Working hours: 30 | Collective
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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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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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, 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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, graphs); experience with analysis and processing of large volumes of data; development of reproducible scientific software; proficiency in Python and libraries (Pandas/NumPy and PyTorch/TensorFlow/Scikit
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distinguer, d'un point de vue statistique, plusieurs processus ponctuels marqués et à identifier les statistiques discriminantes les plus pertinentes. Ces statistiques sont construites à partir de graphes
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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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use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities are experimentally driven and
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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