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 employer: National Research Council of Canada
At NRC, we pride ourselves on being an exceptional employer that fosters a collaborative and innovative work culture. Our hybrid Data Scientist role offers not only competitive benefits but also ample opportunities for professional growth through engagement with Canadian SMEs and cutting-edge analytics projects. Join us in a dynamic environment where your contributions directly impact client success and drive technological advancements.
Contact Details:
National Research Council of Canada Recruitment Team
