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learning, physics-informed neural networks, graph neural networks, transformers, convolutional defiltering methods, etc.) for the integration in multi-physics simulation codes You will develop code for and
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core component is the development of Explainable AI (XAI) frameworks, particularly counterfactual explainability, to ensure model transparency. The project also incorporates Graph Neural Networks (GNNs
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the Division; assists Division faculty with preparation of student materials such as course manuals, handouts, examinations, and grade postings, as well as preparation of manuscripts, articles, charts, graphs
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compile data for various analyses and perform calculations to prepare spreadsheets, graphs, and charts. Manage daily administrative operations, preparing reports and documents to meet compliance
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to anticipate and solve most lab issues with little supervision, preparation of graphs and advanced data analyses, and ability to lead report and/or publication writing efforts. The Research Project Coordinator
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Dortmund, we invite applications for the Multidimensional Omics Data Analysis Research Group: Scientist / Postdoc (m/f/d) You will be responsible for: Development and implementation of a knowledge graph
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AI systems capable of generating data such as text, images, sounds, graphs and other data types. Students will explore the core principles behind generative models, including advanced transformer
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systems architecting AI/ML-driven clinical and operational decision support Digital health and learning health systems Healthcare operations, resource allocation, and workflow optimization Network, graph
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to neural population coding. As a starting point, we will build upon recent advances in graph neural networks (GNNs), particularly those described by which offer a promising architecture for modelling
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answer maps accordingly. We use knowledge graphs to model these transformations and apply AI methods to scale them up across large map repositories, enabling users to explore many ways maps can be reused