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• Familiarity with LLM in-context learning and prompt engineering • Basic understanding of modern LLM models, ecosystems, and pipelines, including retrieval-augmented generation, tools/chains, and LLM agents
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modeling and coordination of interdependent infrastructure systems and their subsystems (such as networks of transportation, gas, electricity grid), multi-agent reinforcement learning for distributed
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focus of the dissertation will be on advancing AI agent-based systems within the MEP construction supply chain. The position is located at the Faculty of Built Environment within the Real Estate
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. The ideal candidate is passionate about graduate education, committed to evidence-based practice, and eager to support students’ professional development within a collaborative academic community. The
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to join our team. In this role, you will design, develop, and deploy state-of-the-art generative AI models, directly impacting our platform's ability to provide interactive and intelligent tools for our
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pathogenesis and their potential as therapeutic agents and diagnostic tools. Using in vitro, ex vivo, and in vivo models, the lab studies EVs derived from amniotic fluid stem cells (AFSCs) and other perinatal
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tools, text-to-video, and related models. Experience with practical implementation/integration/deployment of AI powered tools, such as local models, APIs/online services, agents, and coding assistants
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Sciences Department beginning August 2026. This will be a full-time, on-site role, based in Keene, NH. We seek faculty who can create an inclusive, student-centered learning environment, integrate real-world
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model, competency models, and simulation standards of practice. Educates staff on policies and procedures, supporting practice changes based on evidence-based guidelines. Leadership Contributes
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UW into an AI-powered University. This role will work with various campus stakeholders, IT teams and external partners to drive innovation through consulting, evangelism, agent-building, and AI model