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California State University, San Bernardino | San Bernardino, California | United States | 2 months ago
learning, including LLM, to analyze survey data. The ideal candidate will have basic to intermediate Python skills for data analysis and a strong interest in developing AI/ML models. Experience in web
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; Solid knowledge of statistics; Experience using machine learning methods and large language models (LLMs) for research; Experience in building and/or using large cultural datasets. We encourage
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models (LLMs) have become significantly more difficult to detect, as a poisoned parameter update is harder to recognise than a poisoned dataset. Therefore, several methods have been developed recently to
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. Do you want to know more about LIST? Check our website: https://www.list.lu/ How will you contribute? The AI Readiness and Assessment (AIRA) research group at LIST is seeking a highly motivated
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regulated environments. Experience with enterprise AI platforms, LLM application frameworks, retrieval-augmented generation, vector databases, workflow automation, or agentic AI patterns. Experience with
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at the intersection of learning sciences and artificial intelligence, with particular focus on large language models (LLMs), generative AI (GenAI), and assistive AI as tools and partners in learning, identity
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Description The eBRAIN Lab (https://ebrain4everyone.com ) in the Division of Engineering at New York University Abu Dhabi invites applications for a Post-Doctoral Associate position, to work in the
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timely research question: How can Large Language Models (LLMs) and intelligent agents support transparent, scalable, and auditable clinical data harmonization? We are particularly interested in: LLM-driven
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members have won Nobel Prizes for these ideas - the first business school to achieve this accomplishment. For more information about the University of Chicago Booth School of Business, please visit: http
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | 3 months ago
qualitative model-quality concepts into measurable checks. - 25% - LLM/RAG test-generation pipeline. Design, implement, and iterate prompts, retrieval workflows, model parsers, and orchestration code