Postdoctoral Fellowship in LLM: This postdoctoral fellowship offers an exceptional opportunity for researchers interested in advancing methodologies and theories related to uncertainty quantification and reasoning in large language models. The selected candidate will join a dynamic research environment focused on significant problems in machine learning and natural language processing.
Designation
Postdoctoral Fellow
Research Area
- Uncertainty Quantification
- Reasoning in Large Language Models
- Probabilistic Machine Learning
- Interpretability in Machine Learning
Location
University of Toronto, Canada
Eligibility/Qualification
- PhD or equivalent degree in Computer Science, Statistics, or a closely related field.
- Significant experience with large language models.
- Strong technical research skills, ideally with multiple publications in top-tier machine learning and natural language processing venues (e.g., NeurIPS, ICML, ICLR, ACL).
- Strong programming skills.
- Interest in safety and security issues in large language models.
Job Description
The successful candidate will:
- Lead research projects at the intersection of uncertainty quantification and reasoning in large language models.
- Contribute to methodological and theoretical advancements in the specified research areas.
- Collaborate with a team and participate in discussions pertinent to safety and security in AI.
How to Apply
Interested applicants must submit their applications through the provided application form, which should include:
- Curriculum Vitae
- Two representative publications (preprints are acceptable)
- Statement of research (2 pages) detailing prior research experience and future plans
- Link to Github account
- 2-3 letters of recommendation (solicited post-initial review)
Applicants are encouraged to email the research lead after submitting their application.
Last Date to Apply
- January 9, 2026 (Applications submitted after this date may still be considered.)
For more information, potential candidates can reach out prior to applying.
