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developing new modeling strategies that can integrate heterogeneous public data into model development. Another major research interest is the use of large language model (LLM) techniques to generate highly
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: https://www.cu.edu/employee-services/benefits-wellness . Why work for the University? We have AMAZING benefits and offer exceptional amounts of holiday, vacation and sick leave! The University of Colorado
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language models (LLMs) approximate, extend, or diverge from human expertise across domains. The starting date is negotiable but should be no later than November 1, 2026. The position is fully funded until
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collaboration among partners.Research Conduct literature reviews. Support data collection and analysis. Develop and evaluate NLP algorithms and models. Relevant NLP tasks include: ASR, (local) LLMs, open-domain
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combinations that support the creation of innovative and sustainable food products. Another example is using large language model (LLM) tools to automatically extract and structure fragmented information from
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the ACK project. Design, development and validation of RAG-LLM components. Validation of cognitive state in the RAG assistant. Validation of hybrid human-automated assessment. Apoyo en la investigación para
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Intelligence (DFKI), invites applications for a postdoctoral research position. The group develops foundation models, large language models (LLMs), and agentic AI systems for scientific discovery, engineering
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such as a CPA, CFP, J.D., AB, LLM, MBA, CSPG or AEP One or more years of experience in the field of gift planning Ability to communicate effectively, both orally and in writing A demonstrated working
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Inria, the French national research institute for the digital sciences | Sophia Antipolis, Provence Alpes Cote d Azur | France | 2 days ago
, and ultimately DICOM files) in the context of lung cancer clinical research. The project seeks to build an LLM-based pipeline that assists ARCs by pre-filling structured clinical data, flagging
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, persistent, and safe. Large language models (LLMs) are often adapted after training through fine-tuning, knowledge editing, and activation steering. While efficient, their effects are fragile. Interventions