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Jes Frellsen

3 months ago

PhD in Uncertainty Quantification for Large Language Models at DTU Compute Technical University of Denmark in Denmark

Degree Level

PhD

Field of study

Computer Science

Funding

3-year PhD position starting in fall 2026. Funding details are not specified in the post, but this is a paid doctoral opening at DTU Compute.

Deadline

Expired

Country flag

Country

Denmark

University

Technical University of Denmark

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Keywords

Computer Science
Information Technology
Mathematics
Natural Language Processing
Uncertainty Analysis
Statistics
Large Language Models

About this position

DTU Compute is hiring a PhD student to work on uncertainty quantification for large language models. The project focuses on making LLMs more trustworthy by enabling them to quantify confidence in their outputs using principled uncertainty quantification methods that scale to modern model sizes.

The position is announced by Jes Frellsen, Associate Professor at the Technical University of Denmark (DTU). The project is in collaboration with Christian Hardmeier (ITU/P1) and Hannah Rohde (University of Edinburgh).

Research areas: probabilistic machine learning, uncertainty quantification, large language models, generative AI, trustworthy AI, natural language processing.

Eligibility/preferences: an ideal candidate has a background in probabilistic ML and some experience with LLMs. The post is a 3-year PhD starting in fall 2026.

Deadline: 7 August 2026.

How to apply: submit your application through the DTU jobs portal using the provided link. Review the job posting carefully and prepare the required application documents before the deadline.

Funding details

3-year PhD position starting in fall 2026. Funding details are not specified in the post, but this is a paid doctoral opening at DTU Compute.

What's required

Applicants should have a background in probabilistic machine learning and some experience with large language models. The post implies a research-oriented PhD candidate suitable for work on uncertainty quantification methods scaled to modern model sizes.

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Professors

Associate Professor

University Name
Country flag

Denmark

Research Interests

Radiology

Medical Imaging

Transfer Learning

Artificial Neural Network

Rheology