Centres for Doctoral Training

UKRI AI Centre for Doctoral Training in Biomedical Innovation, led by Professor Ian Simpson, will teach students to use AI to improve the diagnosis, understanding, management, and treatment of disease. Students will be trained in ethical, secure, and responsible research methods throughout the programme. They will be primed to practise responsible and trustworthy use of AI in research, which is fundamental to maintaining the public’s trust in the use of health data. A wealth of expertise from across the University, national bodies, external partners and expert advisory and patient involvement and engagement groups will support the programme.

Students in the CDT in Biomedical Innovation will work with external partners in the private and public sectors to ensure their research addresses critical challenges in healthcare maximising the potential for AI to have a positive impact on society. The CDT will build upon the expertise of the existing CDT in Biomedical AI.

Advertising will commence shortly for our fully-funded PhD projects for September 2024 entry.

Please complete our expression of interest form by clicking the link below and sign-up to our mailing list. We will keep you informed of progress and let you know when applications are open.

Link to expression of interest form

Link to Biomedical Innovations CDT website

UKRI AI Centre for Doctoral Training in Responsible and Trustworthy in-the-world NLP will be led by the School of Informatics in collaboration with Edinburgh College of Art, Edinburgh Law School, and the Schools of Mathematics and Psychology, Philosophy and Language Sciences, and will be hosted by the new Edinburgh Futures Institute. Professor John Vines will direct the CDT.

Natural Language Processing underpins many leading-edge AI applications such as personal assistants (such as Siri, and Alexa), chatbots (such as ChatGPT) and generative AI applications that use text prompts to generate other media (such as Midjourney, DALL-E). These technologies enable new ways of working and accessing services and knowledge but are accompanied by risks.  

Understanding how complex AI systems that utilise natural language, such as large language models (LLMs), operate in the world is critical to ensuring a responsible, sustainable, and socially sensitive design that steers clear of common LLM issues such as bias. To achieve this, systems of the future must be built by multidisciplinary, and diverse teams who understand the complexities of responsibly developing, deploying, and overseeing these systems in real-world settings. Students in the UKRI AI Centre for Doctoral Training in Responsible and Trustworthy in-the-world NLP will be trained across the technical, social, design and legal aspects of these systems, and most critically how to collaborate across disciplines in teams of hybrid expertise.

Below is the timeline for key dates for our 2024 studentships:

• 12th and 13th February 2024: Applicant webinars
• 11th March 2024: Applicant deadline
• 11th April – 30th April: Applicant interviews
• Late April / early May 2024: Initial offers made to applicants
• Week of 9 September 2024: Welcome Week at University of Edinburgh (students arrive)
• Week of 16th September 2024: Training commences

UKRI AI Centre for Doctoral Training in Dependable and Deployable Artificial Intelligence for Robotics will be led by Heriot-Watt University in partnership with the University of Edinburgh. Schools of Informatics and Engineering are both hosting the CDT, with Professor Subramanian Ramamoorthy as co-director. Professor Ron Petrick, a Professor of Computer Science at Heriot-Watt University will be the CDT Director.

To make a difference in our homes and workplaces, Robotics and Artificial Intelligence technologies - such as AI assistants for independent living, manufacturing and construction robots or robots working in extreme conditions - need to be safe, dependable, and trustworthy. While AI methods are finding increasing adoption within robotics, these technologies were not originally designed with safety and other human-centred requirements in mind. Making AI suitable for such dependable and deployable products will require a new way of thinking.

CDT-D²AIR will train experts who can think in new ways around researching, designing, building, and deploying dependable and safe robotics and AI solutions.

Machine Learning has a dramatic impact on our daily lives built on the back of improved computer systems. Systems research and ML research are symbiotic. Modern systems research targets the ubiquitous need for efficient ML. ML research, conversely, is directly affected by how methods will be deployed. Furthermore, systems research increasingly explores ML methods to improve systems, and ML research develops such methods.

Major gains are made when the development of ML and systems are symbiotic and co-optimized. This is relevant across a broad spectrum of industries: in-car systems, medical devices, phones, sensor networks, condition monitoring systems, high-performance compute, and high-frequency trading.

This CDT will develop researchers with expertise across the systems-ML stack. Company engagement is an integral part of the programme with built-in internships alongside entrepreneurship training. The PhD programme in Machine Learning Systems will position students for strong, ethically aware technical careers, developing the next generation of leaders.

Students must have a broad understanding of different hardware designs, different platforms, different environments, different models, and different goals beyond their immediate research focus. Individual supervisory teams rarely have this breadth of knowledge. This makes a cohort-based CDT vital, treating ML Systems as a holistic discipline. Cohort interaction, and integration gives students real experience across multiple systems, approaches and methodologies.

Link to Registration of Interest Form

Quantum technology will revolutionise many aspects of life and bring enormous benefits to the economy and society. The new Doctoral Programme in Quantum Informatics will provide advanced training in the structure, behaviour, and interaction of quantum hardware, software, and applications. The training programme spans computer sciences, mathematics, physics, and engineering, and will enable the use of quantum technology in a way that is integrable, interoperable, and impactful, rather than developing the hardware itself.  

The training programme targets three research challenges with a strong focus on end user impact: (i) quantum service architecture concerns how to design quantum networks and devices most usefully; (ii) scalable quantum software is about feasible application at scale of quantum technology and its integration with other software; and (iii) quantum application analysis investigates how quantum technology can be used most advantageously to solve end user problems.  

The programme offers a fully funded intensive 4-year training and research programme that equips students with the skills needed to tackle the research challenges of quantum informatics. The programme includes bespoke training by the National Quantum Computing Centre, and the opportunity to work with over 30 industry partners. Graduates will be able to integrate quantum hardware with high-performance computing, design effective quantum software, and apply this in a societally meaningful way. 

The Centre will not admit new students till 2025/26, recruitment will open later this year.

Students interested in studying Quantum Computing starting in September 2024 can apply to the Doctoral Training Programme in Quantum Computing, which is recruiting students for entry in September 2024 now.

Link to Registration of Interest Form

The CDT offers an interdisciplinary training programme covering technical AI skills, biomedical foundations and individually tailored training on understanding the societal aspects of Biomedical AI.

BioMedAI website

Students benefit from cutting-edge computing and experimental facilities, including a large GPU cluster and eye-tracking, speech, virtual reality, and visualisation labs.

NLP website

The Centre addresses key challenges for managing interactions between robots and their environments, between multiple autonomous systems, and between robots and people.

The centre is supported by Edinburgh’s world-class infrastructure in robotics.

Robotics and Autonomous Systems website