Explore LLM uncertainty quantification and calibration research as a Postdoctoral Fellow with limited hybrid work eligibility at the NRC. This two-year fellowship develops methods for uncertainty quantification in large language model outputs and intermediate features. The work addresses confidence metrics, interpretability, trustworthy AI, and human-AI interaction. You will work autonomously under direct supervision, with guidance from the project lead, and may contribute to related LLM interpretability and evaluation research. Key Responsibilities: Develop uncertainty quantification methods for LLM outputs Apply methods to intermediate model features Investigate certainty metrics for generated text Study applications in safety and evaluation Present research at international venues Requirements: PhD in computer science, mathematics, statistics, physics, or related discipline Recent PhD or completion within six months Experience designing novel AI research projects Experience with deep learning tools and libraries Publication record demonstrating experimental and scientific contributions Advance NRC research on LLM calibration, uncertainty metrics, interpretability, and trustworthy generative AI applications. #J-18808-Ljbffr

📌 Postdoctoral Fellow Llm Uncertainty Quantification (Ottawa)
🏢 National Research Council of Canada
📍 Ottawa