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projects that may involve concepts and priorities such as knowledge-graph-driven data integration, AI-powered literature review, agentic database search assistants, multi-omics analysis tools, life sciences
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in one of the following areas: Machine Learning / Information Retrieval / Knowledge Graph Representation / Recommender Systems Graph Theory/Network Science Python, and up-to-date machine learning
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Research Associate, Arizona Telemedicine Program Posting Number req26712 Department Arizona Telemedicine Program Department Website Link https://medicine.arizona.edu Location Tucson Campus Address Tucson, AZ
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Job Posting Title: Post Doctoral Scholar The Opportunity Lead and contribute research on multimodal data integration, ontology and knowledge graph building for energy domain. Supervise graduate and
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individuals with expertise in one or more of the following areas: Generative AI Agentic AI Graph Representation Learning and Modeling Foundation Models Large Language Models Multimodal Learning Forecasting
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Posting Summary Logo Posting Number RTF00197PO26 USC Market Title Post Doctoral Fellow Link to USC Market Title https://uscjobs.sc.edu/titles/156387 Business Title (Internal Title) Post Doctoral
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: Psychiatry Group or Departmental Website: https://stai.stanford.edu(link is external) https://neuroailab.stanford.edu/(link is external) https://neuroscience.stanford.edu/(link is external) Does this position
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systems, numerical analysis, stochastic problems and stochastic analysis, graph theory and applications, mathematical biology, financial mathematics and mathematical approaches to signal analysis
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Postdoc Position for Computational Genomics, AI Precision Oncology, Cancer Biology and Immunobiology
University of Pittsburgh, Pittsburgh , Pennsylvania, US | Pittsburgh, Pennsylvania | United States | about 2 months agoimmunotherapies, integrating graph neural networks, regulon-aware pooling, and transfer learning with biological regulatory networks. 4) Developing and validating computational biomarkers (IGR burden, TAA burden
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diverse populations. The candidate will be funded primarily by an R01 on method development for statistical and population genetic applications based on the ancestral recombination graph (see recent