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generation ● Experience with retrieval-augmented generation, agentic or multi-stage workflows, or knowledge-integrated models ● A strong publication record in top-tier AI/NLP venues (ACL, EMNLP
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anemia, cancer and palliative care. Patient care needs are identified through interdisciplinary assessments, team care planning, and a patient classification system. A multi-disciplinary approach is used
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progressed capabilities towards exploiting zero-day vulnerabilities. Frontier models show promising performance when combined with a focused knowledge base and multi-agent architectures. However, in most cases
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difficult social situations (multi system impairments and or multiple co-morbidities) referred by a licensed provider. PRIMARY JOB RESPONSIBILITIES Administers treatments and physical agents as determined by
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for research clinical trials based on scope of practice. Collaborate with clinical staff, researchers, outside vendors, and other staff to administer treatment and study protocols. May coordinate multi-site
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 months ago
Institute for the Environment (IE) has a multifaceted mission: (1) To strengthen environmental research capacity across UNC by supporting a multi-disciplinary community of scholars that enhances collaboration
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 1 hour ago
for the Environment (IE) has a multifaceted mission: (1) To strengthen environmental research capacity across UNC by supporting a multi-disciplinary community of scholars that enhances collaboration, increases sharing
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research projects. Developing innovative methodologies for analyzing massive datasets in both cloud-based and on-premises environments, including experience in building agentic AI tools that integrate
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games, AI for content and/or design, multi-agent systems in games, computational creativity, AI for virtual production and virtual worlds, and explainable or ethical AI in games. We especially welcome
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learning with knowledge-based inference, validated by independent experiments and partially supervised by human-in-the-loop systems. A key question will be how agentic AI and foundation models can be