Summary: The University of Edinburgh’s School of Informatics invites applications for a full-time Research Associate position as part of a collaborative project with the Huawei Trustworthy Technology and Engineering Laboratory in Munich. This role focuses on developing a new generation of reliable and trustworthy large language model (LLM) agents.
Designation
Research Associate
Table of Details
| Detail | Information |
|---|---|
| Location | Informatics Forum, Edinburgh, EH8 9AB, UK (Hybrid working) |
| Contract Type | Fixed Term |
| Work Duration | 13 months |
| Job Schedule | Full-time (35 hours per week) |
| Grade | UE07 |
| Salary | £41,064 – £48,822 per annum |
| Number of Openings | 1 |
| Application Deadline | 7 January 2026 |
Research Area
- Large Language Models (LLMs)
- Neuro-symbolic AI
- Machine Learning (ML)
Location
Informatics Forum, Edinburgh, EH8 9AB, UK (Hybrid working options available)
Eligibility/Qualification
- A PhD or nearing completion in Machine Learning (ML), Machine Learning Systems (MLSys), Natural Language Processing (NLP), or related fields.
- Proven track record of research excellence, demonstrated by publications in top-tier venues.
- Experience with foundation models/LLMs, evidenced by published papers and projects on GitHub.
- Experience in neuro-symbolic systems, with relevant publications and projects.
Job Description
The Research Associate will:
- Conduct pioneering research on LLM agents with neuro-symbolic layers to enhance reliability and trustworthiness.
- Assist the TTE-DE team in benchmarking failure models of foundation models and their neuro-symbolic counterparts.
- Write scientific papers documenting the research methodology and findings.
Supervision: This role will be supervised by Dr. Antonio Vergari, a renowned expert in probabilistic machine learning and neuro-symbolic AI.
How to Apply
Interested applicants should provide the following documents:
- CV
- A 1-page cover letter
- A 2-page research statement detailing past experiences and interests relevant to the position
- A list of the three most relevant scientific papers along with a link to a well-maintained codebase
Applications should be submitted by the deadline noted below.
Last Date to Apply
7 January 2026 (by 11:59 PM GMT)
For inquiries, please contact Dr. Antonio Vergari at avergari@ed.ac.uk.
