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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Doctoral scholarship holder Artificial Intelligence - continual graph learning - **Company:** Universiteit Antwerpen - **Location:** Antwerpen, Belgium - **Contract:** Temporary contract - **Skills:** Gesture Recognition, Artificial Intelligence, Artificial Neural Networks, Python (Programming Language), Machine Learning, Pytorch, Deep Learning, Information Technology - **Published:** July 18, 2026 - **Apply:** https://be.indeed.com/viewjob?jk=b7216de67f33c347 ## About the Role * You hold a master's degree in Computer Science, Informatics, Mathematics or closely related field, or you will have obtained it by the time you start work. * You can demonstrate excellent study results. * Your teaching competences are in line with the University of Antwerp's educational vision. * Your research qualities are in line with the faculty and university research policies. * You act with attention to quality, integrity, creativity and cooperation. * You have a strong interest in machine learning/artificial intelligence. * You have a strong interest in graph-based learning (e.g., graph neural networks). * You have experience with deep learning. * You have experience with coding in Python using deep learning toolkits (e.g., PyTorch). * You have good analytical and problem-solving abilities. * You are proficient in English (written and spoken)., o an English proficiency certificate. + A short research proposal aligned with your research interests on continual graph learning. Proposal is maximum 4 pages, excluding references. o The research proposal should include: (1) problem statement and motivation, (2) expected novelty/impact, (3) brief approach (models, algorithms, datasets), (4) evaluation plan, and (5) alignment with IDLab research on continual graph learning at the University of Antwerp. * The selection committee reviews all applications as soon as possible after the application deadline. As soon as a decision is made, we will notify you. If you are still eligible after the pre-selection, you will be informed about the possible next step(s) in the selection procedure. ## Description * You will work actively on the preparation and defence of a PhD thesis in the field 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. Potential application domains include gesture recognition, human activity recognition, energy forecasting (e.g., demand, supply, flexibility) in smart grids, etc. * You will publish scientific articles related to the research project. * You will carry out a limited number of teaching and research support tasks for Department of Electronics and Information and Communication Technology. ## Related Videos - [Getting Started with Machine Learning](https://www.wearedevelopers.com/videos/260-getting-started-with-machine-learning) - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Geometric deep learning for drug discovery](https://www.wearedevelopers.com/videos/264-geometric-deep-learning-for-drug-discovery) - [Graph Neural Networks: What’s behind the Hype?](https://www.wearedevelopers.com/videos/474-graph-neural-networks-what-s-behind-the-hype) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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