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than 20 percent of its 25,000 students are enrolled in graduate course work, studying in disciplines ranging from atomic physics and graph theory to medieval literature and blind rehabilitation. Of 101
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percent of its 25,000 students are enrolled in graduate course work, studying in disciplines ranging from atomic physics and graph theory to medieval literature and blind rehabilitation. Of 101 graduate
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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
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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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to work effectively in an interdisciplinary team. PREFERRED QUALIFICATIONS Experience with one or more of the following: knowledge graphs, graph machine learning, link prediction, representation learning
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degrees through the doctoral level. More than 20 percent of its 25,000 students are enrolled in graduate course work, studying in disciplines ranging from atomic physics and graph theory to medieval
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development and knowledge graph components within the broader platform architecture. Working closely with researchers and operational teams, you translate complex research and business needs into robust, user
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Applied to Neuroscience and Drug Discovery Where to apply Website https://gestiononline.bioef.eus/ConvocatoriasPropiasBiobizkaia/es/Convocatorias… Requirements Research FieldOtherEducation LevelPhD
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expected to gather and analyze data, and graph results, appropriate for use in scientific publications and/or presentation. Minimum Requirements Knowledge equivalent to that which normally would be acquired
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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