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.

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