PhD Candidate in Uncertainty Quantification (On-site)

Job summary

This PhD position focuses on developing methods for uncertainty quantification in large language models (LLMs) to enhance their reliability and trustworthiness. The candidate will collaborate with leading researchers in Denmark and the University of Edinburgh, contributing to significant advancements in machine learning.

Qualifications

• A two-year master's degree (120 ECTS points) or equivalent academic level.
• Experience in probabilistic machine learning, Bayesian deep learning, and uncertainty quantification.
• Proficiency in machine learning frameworks such as PyTorch or JAX.
• Familiarity with training, fine-tuning, and evaluating large language models.
• Experience in academic publishing is advantageous.

Responsibilities

• Develop and scale methods for uncertainty quantification in LLMs.
• Align expressed confidence levels with statistical uncertainty estimates.
• Collaborate with interdisciplinary teams across DTU, ITU, and Edinburgh.
• Publish and present research findings at leading machine learning and NLP conferences.

Skills

• Strong background in machine learning.
• Interest in uncertainty quantification and trustworthy AI.

Education

• A master's degree in a relevant field is required.

Tools

• Machine learning frameworks (e.g., PyTorch, JAX).