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Biomedical Data Science Postdoc Appointment Term: 2 years (can be exended) Appointment Start Date: December 1, 2025 (Flexible) Group or Departmental Website: http://med.stanford.edu/summerhanlab.html (link is
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mathematics/statistics/computer science, willing to specialize their research on large language models (LLMs). The successful candidate will contribute to the development of trustworthy LLMs by focusing
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vary). Experience designing or using AI-assisted research workflows (e.g., LLM-supported literature synthesis, structured extraction of claims/evidence, reproducible AI-in-the-loop pipelines
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, tables, diagrams) into structured representations and formally checkable properties. This includes designing controlled reasoning pipelines that combine LLMs with symbolic constraints (typed logical forms
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 9 hours ago
. The candidate may help develop a projection layer that connects predicted geospatial features and metrics to an open-source large language model (LLM), enabling natural-language queries over forest conditions and
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postdoctoral researcher to join the research group led by Ekaterina Shutova (https://www.shutova.org/) , part of the natural language processing research unit of the Institute for Logic, Language and
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or planning. Experience with ROS2, simulators, LLMs, or underwater robotics is highly valued. Where to apply Website https://apps.udg.edu/contractacioPDI/account/login.aspx?id=recerca&tipus=recerc… Requirements
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the integration of AI components transforms the nature of software systems (SE4AI). From an architectural perspective, the research investigates how the inclusion of AI elements—such as retrainable ML models, LLMs
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and take a lead in the group’s “Improving the quality of Translation/Revision/PEMT output by using automatic speech tools and LLMs” and “Cognitive investigations of Translation/Revision/PEMT practices
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how DNA LLM work, and develop solutions to integrate them into the neural network architectures developed by the lab. - Focus on developing new solutions for the scalability of neural networks and large