Research Associate

University of Edinburgh; Leeds; Hybrid; GBP 40,000 - 50,000; Full time

Job summary

A prestigious educational institution in the UK seeks a Post-Doctoral Research Associate (PDRA) in Probabilistic Machine Learning and Neuro-symbolic AI. The role involves cutting-edge research and collaboration with a leading expert, with opportunities for international travel and hybrid working. Ideal candidates hold a relevant PhD and have a strong research background with documented evidence. This is a full-time role with flexibility. Contact Dr. Antonio Vergari for applications.

Benefits

Funding for international travel to conferences

Access to high-performance computing infrastructure

Qualifications

• PhD or nearing completion in ML, MLSys, NLP or related areas of computer science.
• Evidence of research excellence with publications in top-tier venues.
• Experience with foundation models / LLMs, backed by published papers and GitHub projects.
• Experience with neuro-symbolic systems through publications and GitHub projects.

Responsibilities

• Conduct cutting-edge research in large-language-model agents with neuro-symbolic layers.
• Assist the TTE-DE team with benchmarking failure models of foundation models.
• Write scientific papers documenting the proposed methodology.

Skills

PhD or near completion in ML, MLSys, NLP or related areas of computer science

Track record of research excellence

Experience implementing foundation models / LLMs

Experience implementing neuro-symbolic systems

Education

PhD in a related field

Job description

PDRA – Probabilistic Machine Learning and Neuro-symbolic AI

Supervised by Dr. Antonio Vergari, a leader in tractable probabilistic machine learning and neuro-symbolic AI, this post‑doctoral research associate (PDRA) will collaborate with researchers and engineers from the TTE‑DE Lab.

Key responsibilities include:

• Conducting cutting‑edge research in large‑language‑model agents with neuro‑symbolic layers, building on our lab's pioneering work on reliable and trustworthy ML;
• Assisting the TTE‑DE team with benchmarking different failure models of foundation models and their safer neuro‑symbolic version;
• Writing scientific papers documenting the proposed methodology.

The position provides funding for international travel to attend conferences and offers access to our high‑performance computing infrastructure.

Employment details:

• Full‑time (35 hours per week) with flexibility for part‑time and hybrid working arrangements;
• Open to UK and international applicants; visa sponsorship available.

Skills and attributes for success

• Ph D or near completion in ML, MLSys, NLP or related areas of computer science / engineering / mathematics;
• Track record of research excellence, e.g. publications in top‑tier venues;
• Experience implementing foundation models / LLMs, evidenced by published papers and projects on GitHub;
• Experience implementing neuro‑symbolic systems, evidenced by published papers and projects on GitHub.

Application information

• CV
• 1‑page cover letter
• 2‑page research statement highlighting how past experience and current interests align with the position and the work in the april Lab
• List of the top three most relevant scientific papers and a link to a well‑maintained codebase

Contact: Antonio Vergari – [e-mail]

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