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University of Edinburgh

Research Associate

City of Edinburgh Hybrid 2-5 yrs exp£41k – £49k / year
Large Language ModelsNatural Language ProcessingMachine LearningCognitive ModelingDistributed Computing

Requirements

Candidates should have a strong background in AI research, specifically in NLP, machine learning, or cognitive modeling. Applicants must be able to design novel model architectures and demonstrate proficiency in developing robust research code for large-scale training.

Job Description

UE07: £41,064 - £48,822 per annum

CSE / School of Informatics

Full Time: 35 hours per week

Fixed term: 14 Months 

Vacancies available: 5 



The School of Informatics at the University of Edinburgh invites applications for several Post-Doctoral Research Associates (PDRAs), to do research on novel architectures for Large Language Models, under the supervision of Professors Frank Keller, Mirella Lapata, Amos Storkey, and Ivan Titov.


The Opportunity

Five positions are part of the Science of Fundamental AI Research (SOFAIR) Lab, a partnership between UCL and the Universities of Cambridge, Edinburgh, and Oxford. SOFAIR is one of two new national AI research labs funded as part of a £60 million investment from UK Research and Innovation (UKRI). SOFAIR will bring together researchers from across computer science, mathematics, statistics and neuroscience to explore new AI architectures and those designed to run on widely available hardware. This will mean cutting-edge AI will be more widely accessible for everyone, including researchers and institutions.

The PDRAs will be part of the School of Informatics, University of Edinburgh, which is ranked among the top schools in Europe for AI research according to CSRankings. They will be supervised by Professors Frank Keller, Mirella Lapata, Amos Storke, and Ivan Titov, who are leaders in NLP, machine learning, and cognitive modeling. The job holders will collaborate closely with the SOFAIR partners at UCL, Cambridge, and Oxford.

The PDRAs will be responsible for developing new fundamental AI modelling and learning methods that go beyond standard transformers and next-token prediction. The work will explore modular, compositional and compute-adaptive models, including architectures and learning paradigms inspired by neurobiology. The work will also investigate sparse or structured reasoning and new training approaches, such as reinforcement-learning hybrids and gradient-free methods. A central goal of SOFAIR is to improve reasoning, efficiency and interpretability while enabling training and inference across heterogeneous, distributed, small-memory hardware. Specific duties will include designing and implementing novel model architectures, developing scalable training algorithms, and conducting controlled experiments to evaluate reasoning, efficiency, interpretability, and generalisation. The post holders will train and benchmark large models using multi-GPU and distributed-computing infrastructure, including the IsambardAI supercomputer, and will be expected to develop robust research code, analyse model behaviour, and disseminate findings through publications and presentations.

These positions include funding for international travel to attend conferences as well as a dedicated compute allocation on IsambardAI, the UK’s national AI supercomputer comprising over 5000 Nvidia GH200 GPUs. 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
  • A 1-page cover letter
  • A 2-page research statement that highlights how you’re the past experience and current interests align with this position and the work in SOFAIR
  • A list of your 3 most relevant scientific papers and a link to a well-maintained codebase
     

Applications without the above material will be rejected.

 

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. 

 

Championing equality, diversity, and inclusion:

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. 

 

We welcome applications from all qualified candidates and particularly encourage applications from [insert demographic(s)] candidates, as we acknowledge they are currently underrepresented in our area/team/at this level.

 

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 may be able to sponsor the employment of international workers in this role.  This will depend on a number of factors specific to the successful applicant.  

 

Key dates to note:

The closing date for applications is 7 September 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.


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.

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 ModelsNatural Language ProcessingMachine LearningCognitive ModelingDistributed ComputingMulti-GPU TrainingModel Architecture DesignScalable Training AlgorithmsReinforcement LearningGradient-free MethodsPythonResearch Code DevelopmentData AnalysisScientific WritingCompositional Modeling