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-augmented generation (RAG) approaches Systems and mathematical modeling of biological or complex systems Natural language processing and machine learning Data harmonization and integration Record of research
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in order to artificially bolster populations for augmentative biological control schemes. -Train and supervise undergraduate and graduate students. -Collect, organize, and analyze data for use in peer
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, such as generative AI, smart wearables, and augmented or mixed reality, can enable ‘total communication’, supporting people’s ability to connect and express themselves in everyday and digital contexts
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, etc. - ML-augmented numerical method development. - High-performance computing (HPC). - Quantum algorithm design. - Error correction or error mitigation. City
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instrumentation. Experience with Python, model/API integration, retrieval-augmented generation, tool-using agents, scientific databases, and automated experimental platforms is highly desirable. The candidate is
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material, device, and process scales Plan and execute experiments to support and augment modeling efforts Conduct simulations using state-of-the-art tools to supplement model development Contribute
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optimization of large language models (LLMs) and related architectures for generative tasks, continuous learning, indexing or retrieval, support of retrieval augmented generation over many data points from long
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Keystone project on Addressing socio-technical limitations of Large Language Models (LLMs), particularly for medical and social computing https://adsolve.github.io/ ). The current role involves augmenting
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staffing, employment, or staff augmentation. For postdoctoral researchers, it is not a substitute for a formal ORNL postdoctoral appointment. For professionals, it is not a vehicle for project-based
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dialogue, where meaning emerges through grounding, clarification and feedback. A possible application domain for this research is augmented and mixed reality environments, which require real-time perception