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. Relevant skills could include statistical analysis, data management and collection, causal inference, network analysis, graph theory, visualizations, and online tool development. Experience in conducting
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, museum professionals, and other partners in the Hybrid Intelligence network. Where to apply Website https://www.academictransfer.com/en/jobs/362707/postdoc-position-on-multimodal-… Requirements Specific
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learning libraries (such as TensorFlow, PyTorch), along with experience in structural modeling tools (e.g., Vienna, Rosetta, RNAstructure) and graph neural networks or transformers applied to molecular
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science. It offers a novel perspective on understanding and managing uncertainty in large-scale infrastructure systems. For more details, see https://research.chalmers.se/en/project/12873 Who we are looking
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. Experience applying machine learning to networking problems, for example, reinforcement learning, graph neural networks, or uncertainty quantification. A track record of publications in leading networking
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advanced post-training techniques to ensure foundation health models reason accurately over medical knowledge graphs and clinical instructions while verifying factuality and safety boundaries. Agentic
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. Experience applying machine learning to networking problems, for example, reinforcement learning, graph neural networks, or uncertainty quantification. A track record of publications in leading networking
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and collection, causal inference, network analysis, graph theory, visualizations, and online tool development. Experience in conducting online controlled experiments is also desired, but not required