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The University of Edinburgh
Research Associate
City of Edinburgh Hybrid 2-5 yrs exp£41k – £49k / year
Large Language ModelsNeuro-symbolic ModelsProbabilistic Machine LearningConstrained GenerationBiological Data Analysis
Requirements
Candidates must have a background suitable for a Post-Doctoral Research Associate role with expertise in AI and machine learning. Application requires a CV, cover letter, research statement, relevant scientific papers, and a codebase link.
Job Description
Job Description
UE07: £41,064 - £48,822 per annum
CSE / School of Informatics
Full Time: 35 hours per week
Fixed term: 24 months
The School of Informatics, University of Edinburgh invites applications for a 2-year Post-Doctoral Research Associate (PDRA), to do research on reliable constrained generation with large language models via neuro-symbolic models, with applications to biological data, under the supervision of Dr Antonio Vergari and in collaboration with Prof. Wouter Boomsma from the University of Copenhagen.
The Opportunity
Don’t just see the bigger picture, help create it.
The University isn’t just any employer. We are part of a local community and a contributor to global thinking, progress and research. In Edinburgh, you are in one of the world’s most attractive cities with active arts and social sectors, while working in a University that has made significant contributions to society, medicine, physics and teaching for over four centuries. Our people have helped create the modern world and are working on more ground-breaking technologies and practices.
The University of Edinburgh is a world-class organisation. We look for the best in the field across all disciplines and provide a working environment where academics can develop their careers and passion for their chosen subject area. We offer the full range of academic roles and have a genuine focus on our student's performance and wellbeing.
The position is in collaboration with Prof. Wouter Boomsma from the University of Copenhagen. As part of this project, we aim to investigate the theoretical and practical foundations of reliable controlled generation with large language models (LLMs). We will do so by applying principled probabilistic neuro-symbolic techniques and advance the current state-of-the-art in terms of reliability and efficiency. This ambitious goal will be tested on sequential biological data.
The PDRA will be part of the April Lab at the School of Informatics, University of Edinburgh which is ranked among the top schools in Europe for AI research according to CSRankings . The PDRA will be supervised by Dr. Antonio Vergari, a leader in tractable probabilistic machine learning and neuro-symbolic AI, and will collaborate with researchers from Prof. Boomsma’s lab.
The PDRA role involves 1) conducting cutting-edge research in LLM constrained generation with neuro-symbolic layers, building on our lab’s pioneering research on reliable and trustworthy ML; and 2) assisting Prof. Boomsma’s team with applications to biological data; 3) writing scientific papers documenting the proposed methodology and presenting them at conferences.
This position includes funding for international travel to attend conferences and offers access to our HPC infrastructure. The position is open to UK and international applicants, with visa sponsorship available. This post is advertised as full-time (35 hours per week), however, we are open to considering part-time or flexible working patterns. We are also open to considering requests for hybrid working (on a non-contractual basis) that combines a mix of remote and regular on-campus working.
View The Full Job Description
How to apply:
Please Include The Following Documents In Your Application
As a valued member of our team, you can expect:
The University of Edinburgh holds a Silver Athena SWAN award in recognition of our commitment to advance gender equality in higher education. We are members of the Race Equality Charter, and we are also a Stonewall Proud Employer, actively promoting LGBTQ+ equality.
Prior to any employment commencing with the University, you will be required to evidence your right to work in the UK. Further information is available on our right to work webpages (opens new browser tab)
The University is able to sponsor the employment of international workers in this role. If successful, an international applicant requiring sponsorship to work in the UK will need to satisfy the UK Home Office’s English Language requirements and apply for and secure a Skilled Worker Visa.
Key dates to note
The closing date for applications is 28 August 2026.
Unless stated otherwise the closing time for applications is 11:59pm UK time. If you are applying outside the UK the closing time on our adverts automatically adjusts to your browsers local time zone.
Interviews will be held on a rolling basis.
About Us
As a world-leading research-intensive University, we are here to address tomorrow’s greatest challenges. Between now and 2030 we will do that with a values-led approach to teaching, research and innovation, and through the strength of our relationships, both locally and globally.
About The Team
Informatics is the study of how natural and artificial systems store, process and communicate information. Research in Informatics promises to take information technology to a new level, and to place information at the heart of 21st century science, technology and society. The School enjoys collaborations across many disciplines in the University, spanning all three College, and also participates as a strategic partner in the Alan Turing Institute and is home to a number of Centres for Doctoral Training.
The School provides a fertile environment for a wide range of studies focused on understanding computation in both artificial and natural systems. It attracts students around the world to study in our undergraduate and postgraduate programmes. Informatics is one of seven schools in the College of Science and Engineering, at the University of Edinburgh. It is recognised for the employability of its graduates, its contributions to entrepreneurship, and the excellence of its research. Since the first Research Assessment Exercise in 1986, Informatics at Edinburgh has consistently been assessed to have more internationally excellent and world-class research than any other submission in Computer Science and Informatics. The latest REF 2021 results have again confirmed that ours is the largest concentration of internationally excellent research in the UK. This contributes to our ranking of consistently being in the top 30 world-wide.
We aim to ensure that our culture and systems support flexible and family-friendly working and recognise and value diversity across all our staff and students. The School has an active programme offering support and professional development for all staff; providing mentoring, training, and networking opportunities.
UE07: £41,064 - £48,822 per annum
CSE / School of Informatics
Full Time: 35 hours per week
Fixed term: 24 months
The School of Informatics, University of Edinburgh invites applications for a 2-year Post-Doctoral Research Associate (PDRA), to do research on reliable constrained generation with large language models via neuro-symbolic models, with applications to biological data, under the supervision of Dr Antonio Vergari and in collaboration with Prof. Wouter Boomsma from the University of Copenhagen.
The Opportunity
Don’t just see the bigger picture, help create it.
The University isn’t just any employer. We are part of a local community and a contributor to global thinking, progress and research. In Edinburgh, you are in one of the world’s most attractive cities with active arts and social sectors, while working in a University that has made significant contributions to society, medicine, physics and teaching for over four centuries. Our people have helped create the modern world and are working on more ground-breaking technologies and practices.
The University of Edinburgh is a world-class organisation. We look for the best in the field across all disciplines and provide a working environment where academics can develop their careers and passion for their chosen subject area. We offer the full range of academic roles and have a genuine focus on our student's performance and wellbeing.
The position is in collaboration with Prof. Wouter Boomsma from the University of Copenhagen. As part of this project, we aim to investigate the theoretical and practical foundations of reliable controlled generation with large language models (LLMs). We will do so by applying principled probabilistic neuro-symbolic techniques and advance the current state-of-the-art in terms of reliability and efficiency. This ambitious goal will be tested on sequential biological data.
The PDRA will be part of the April Lab at the School of Informatics, University of Edinburgh which is ranked among the top schools in Europe for AI research according to CSRankings . The PDRA will be supervised by Dr. Antonio Vergari, a leader in tractable probabilistic machine learning and neuro-symbolic AI, and will collaborate with researchers from Prof. Boomsma’s lab.
The PDRA role involves 1) conducting cutting-edge research in LLM constrained generation with neuro-symbolic layers, building on our lab’s pioneering research on reliable and trustworthy ML; and 2) assisting Prof. Boomsma’s team with applications to biological data; 3) writing scientific papers documenting the proposed methodology and presenting them at conferences.
This position includes funding for international travel to attend conferences and offers access to our HPC infrastructure. The position is open to UK and international applicants, with visa sponsorship available. This post is advertised as full-time (35 hours per week), however, we are open to considering part-time or flexible working patterns. We are also open to considering requests for hybrid working (on a non-contractual basis) that combines a mix of remote and regular on-campus working.
View The Full Job Description
How to apply:
Please Include The Following Documents In Your Application
- CV
- 1-page cover letter
- 2-page research statement that highlights how the past experience and current interests of the candidate align with this position and the work in the April Lab.
- A list of the 3 most relevant scientific papers and a link to a well-maintained codebase.
As a valued member of our team, you can expect:
- A competitive salary.
- An exciting, positive, creative, challenging and rewarding place to work.
- To be part of a diverse and vibrant international community.
- Comprehensive Staff Benefits, including generous annual leave entitlement, a defined benefits pension scheme, a wide range of staff discounts, family-friendly initiatives, and flexible work options. Check out the full list on our staff benefits page (opens in a new tab) and use our reward calculator to discover the value of your pay and benefits.
The University of Edinburgh holds a Silver Athena SWAN award in recognition of our commitment to advance gender equality in higher education. We are members of the Race Equality Charter, and we are also a Stonewall Proud Employer, actively promoting LGBTQ+ equality.
Prior to any employment commencing with the University, you will be required to evidence your right to work in the UK. Further information is available on our right to work webpages (opens new browser tab)
The University is able to sponsor the employment of international workers in this role. If successful, an international applicant requiring sponsorship to work in the UK will need to satisfy the UK Home Office’s English Language requirements and apply for and secure a Skilled Worker Visa.
Key dates to note
The closing date for applications is 28 August 2026.
Unless stated otherwise the closing time for applications is 11:59pm UK time. If you are applying outside the UK the closing time on our adverts automatically adjusts to your browsers local time zone.
Interviews will be held on a rolling basis.
About Us
As a world-leading research-intensive University, we are here to address tomorrow’s greatest challenges. Between now and 2030 we will do that with a values-led approach to teaching, research and innovation, and through the strength of our relationships, both locally and globally.
About The Team
Informatics is the study of how natural and artificial systems store, process and communicate information. Research in Informatics promises to take information technology to a new level, and to place information at the heart of 21st century science, technology and society. The School enjoys collaborations across many disciplines in the University, spanning all three College, and also participates as a strategic partner in the Alan Turing Institute and is home to a number of Centres for Doctoral Training.
The School provides a fertile environment for a wide range of studies focused on understanding computation in both artificial and natural systems. It attracts students around the world to study in our undergraduate and postgraduate programmes. Informatics is one of seven schools in the College of Science and Engineering, at the University of Edinburgh. It is recognised for the employability of its graduates, its contributions to entrepreneurship, and the excellence of its research. Since the first Research Assessment Exercise in 1986, Informatics at Edinburgh has consistently been assessed to have more internationally excellent and world-class research than any other submission in Computer Science and Informatics. The latest REF 2021 results have again confirmed that ours is the largest concentration of internationally excellent research in the UK. This contributes to our ranking of consistently being in the top 30 world-wide.
We aim to ensure that our culture and systems support flexible and family-friendly working and recognise and value diversity across all our staff and students. The School has an active programme offering support and professional development for all staff; providing mentoring, training, and networking opportunities.
Education
Postgraduate Degree
Skills
Large Language ModelsNeuro-symbolic ModelsProbabilistic Machine LearningConstrained GenerationBiological Data AnalysisScientific WritingAI ResearchTractable Probabilistic ML