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systems • Healthcare operations, resource allocation, and workflow optimization • Network, graph, and agent-based modeling for care delivery • Health equity, patient access, and system resilience • Multi
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Leibniz-Institute for Food Systems Biology at the Technical University of Munich | Freising, Bayern | Germany | 29 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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- Provisional Positions Department's Website: https://cosmos.ualr.edu/ Summary of Job Duties: The Graduate Research Assistant will develop machine learning and artificial intelligence (ML/AI)-driven socio
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- Provisional Positions Department's Website: https://cosmos.ualr.edu/ Summary of Job Duties: The Graduate Research Assistant will transition socio-computational models to usable tools. The Graduate Research
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, maintains, and updates queries and reports to fulfill recurring data needs for internal and external reporting requirements. Produces tables, graphs, dashboards, and narrative analysis of data to easily
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, coursework, or training in data processing techniques and methods for multimodal biomedical data or knowledge graphs. Contact Information: Heather Viana 10 Shattuck St Boston, MA 02115 Contact Email
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/or Korte), 3. Conformal deformations of metric measure spaces and/or general regularity and convergence for graph-based machine learning using stochastic game theory and theory of metric spaces (with
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, R Shiny, and frameworks for Large Language Models (LLMs) or Graph Neural Networks (GNNs). We are an equal opportunity employer, and all qualified applicants will receive consideration for employment
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, survey and interview instrument design and testing, and basic data analysis (dependent on project needs and proficiency with data analysis software, including Excel, creation of charts and graphs
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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