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π’ France β PhD Position in Graph Machine Learning at Institut Polytechnique de Paris
π University: Institut Polytechnique de Paris
π Country: France
π Fields:
Computer Science, Data Science, Engineering, Applied Mathematics, Signal Processing
Are you passionate about advancing the frontiers of machine learning and signal processing, and eager to make a real impact on how massive and complex datasets are analyzed in todayβs digital world? If you are looking to develop expertise in graph neural networks, graph signal processing, and scalable learning, this fully funded PhD position at the Institut Polytechnique de Paris could be your ideal next step.
The PhD project centers on βSignal-aware Graph Summarization with GNN Guarantees (SIGMA).β As digital networks grow ever larger and more complexβspanning social media, biological systems, recommender engines, and the webβthe need for efficient, scalable analysis tools becomes critical. Graphs are the natural representation for such interconnected data, but their sheer scale poses significant challenges for storage, computation, and effective learning.
π Find out more and Apply Now:
β οΈ Android users: If it doesn't open, use "Open in browser"
https://phdfinder.com/2026/10/15/france-phd-position-in-graph-machine-learning-at-institut-polytechnique-de-paris/
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π University: Institut Polytechnique de Paris
π Country: France
π Fields:
Computer Science, Data Science, Engineering, Applied Mathematics, Signal Processing
Are you passionate about advancing the frontiers of machine learning and signal processing, and eager to make a real impact on how massive and complex datasets are analyzed in todayβs digital world? If you are looking to develop expertise in graph neural networks, graph signal processing, and scalable learning, this fully funded PhD position at the Institut Polytechnique de Paris could be your ideal next step.
The PhD project centers on βSignal-aware Graph Summarization with GNN Guarantees (SIGMA).β As digital networks grow ever larger and more complexβspanning social media, biological systems, recommender engines, and the webβthe need for efficient, scalable analysis tools becomes critical. Graphs are the natural representation for such interconnected data, but their sheer scale poses significant challenges for storage, computation, and effective learning.
π Find out more and Apply Now:
β οΈ Android users: If it doesn't open, use "Open in browser"
https://phdfinder.com/2026/10/15/france-phd-position-in-graph-machine-learning-at-institut-polytechnique-de-paris/
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