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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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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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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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Leibniz-Institute for Food Systems Biology at the Technical University of Munich | Freising, Bayern | Germany | 16 days ago
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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-mail and by post will not be considered. Where to apply Website https://academicpositions.com/ad/empa/2026/phd-position-in-hierarchical-graph-n… Requirements Research FieldComputer scienceYears
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Leibniz-Institute for Food Systems Biology at the Technical University of Munich | Freising, Bayern | Germany | 16 days ago
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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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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tailored computational methods are needed. This project aims at combining probabilistic machine learning methods with prior knowledge in the form of graphs to analyze and predict food-effector systems. Key
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Optimization: Mathematical Phylogenetics During this project, you will work on fundamental graph-theoretic and algorithmic problems in mathematical phylogenetics. Job description The Discrete Mathematics and
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, for their analysis 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