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on Graphs: Symmetry Meets Structure (LOGSMS). The field of Machine Learning on Graphs aims to extract knowledge from graph-structured and network data through powerful machine learning models. Designing
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at the University of Twente is looking for two PhD candidates to join the research team of Dr. Gaurav Rattan. The positions are funded by the NWO VIDI project Learning on Graphs: Symmetry Meets Structure (LOGSMS
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maps. Knowledge graphs can be used to model these transformations and to link geodata sources to questions. In this project we will apply symbolic and sub-symbolic AI methods to scale this up across
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answer maps accordingly. We use knowledge graphs to model these transformations and apply AI methods to scale them up across large map repositories, enabling users to explore many ways maps can be reused
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the response model from reactive to proactive. The goal is to increase transparency and trust in the DNS namespace. Key research activities will include applying machine learning and graph-based techniques
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families (e.g., generative models or graph/equivariant neural networks) to accelerate candidate discovery and hypothesis generation. Disseminate research findings through publications, conference
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of the future, striving to improve healthcare and society as a whole. Where to apply Website https://www.academictransfer.com/en/jobs/356668/phd-in-machine-learning-for-dru… Requirements Specific Requirements
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integration and knowledge representation (e.g. FAIR data, semantic interoperability, ontologies, knowledge graphs); Machine learning and generative AI for health (e.g. multimodal patient modeling, predictive
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the existing In Orbit Demonstration to the future preparatory phases. Candidates interested are encouraged to visit the ESA website: http://www.esa.int Field(s) of activity for the internship You can choose
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theory, and spectral graph theory. The Posdoc will be supervised by Anurag Bishnoi. You will have the opportunity to collaborate with Postdocs, PhD candidates, and other faculty members of the research