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Field
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of you Required PhD in machine learning, physics, or a related field. Established expertise in deep learning (familiarity with graph neural networks, transformers, diffusion and flow based generative
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interoperable methodological framework for AI in gynecological oncology. We integrate symbolic knowledge representation (Ontologies/Knowledge Graphs) with Retrieval-Augmented Generation (RAG) and Large Language
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to the large-scale nature, complexity, and heterogeneity of 6G networks, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal
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Science, Statistical Physics, or any other related field. The skills that we are looking for include: Statistical analysis & causal inference Data management, collection & visualization Social network analysis & graph
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storage and analysis solutions (e.g., key-value stores, object or document storage, graph analytics systems) deployed on HPC computational and storage systems. Co-authorship of peer-reviewed publications
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system to track proposals. Evaluate and perform preliminary analysis of the data using graphs, charts or tables to highlight the key points of the research results collected in accordance with the research
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dynamical systems on graph with modern power grid systems as an application. Education and Experience: Applicants must have recently completed a Ph.D. and have exceptional research potential. Teaching may be
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relevant expertise: A PhD in Computer Science or a closely related field, with specialization in Quantum computing and Graph theory In this role, you will be responsible for conducting research on graph
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include statistical analysis, data management and collection, causal inference, network analysis, graph theory, visualizations, and online tool development. Experience in conducting online controlled
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of Research Experience1 - 4 Additional Information Eligibility criteria • Doctor of Computer Science, Robotics • Skills in formal knowledge representation (ontology, knowledge graph, logic resolution