Job Description
Job Title:  Research Fellow (LLM), School of Computing
Posting Start Date:  03/02/2025
Job Description: 

Job Description

The National University of Singapore invites applications for the position of Research Fellow in the Department of Computer Science, School of Computing (SoC). SoC is strongly committed to research excellence in all its dimensions: Searching for fundamental results and insights, developing novel computational solutions to a wide range of applications, building large-scale experimental systems and improving the well-being of society. We seek to play an active role both internationally and locally in the core and emerging areas of Computer Science and Information Systems.

 

By scaling up data, compute and model size, large language models (LLMs) have gained an impressive and ever growing array of capabilities. The next phase of development will be dominated by the use of LLMs in real world scenarios, and improving LLM reliability will be a crucial and exciting component of this next phase. A research fellow position is available in a joint project across Oxford, Nanyang Technological University and National University of Singapore studying the reliability of LLMs through the lens of uncertainty quantification (UQ), Bayesian inference, conformal prediction, and world models. Along with industrial and government partners, the postholder and project team will use these methods to address current issues with LLMs, such as reward hacking, active learning and testing of LLMs.

Qualifications

  • Applicants should be close to completion or hold a relevant Ph.D/D.Phil.
  • Research experience in the areas of deep learning or large language models.
  • Good publication record.
  • Good communication skills.

 

If you are interested in the position, please send your CV and cover letter to Prof Wee Sun LEE (leews@comp.nus.edu.sg).
 

More Information

Location: Kent Ridge Campus

Organization: School of Computing

Department : Department of Computer Science

Employee Referral Eligible: No

Job requisition ID : 27684