Centres for Doctoral Training

The University is currently running or a partner in five separate Centres for Doctoral Training (CDTs) designed to support the training and development of the next generation of researchers in digital topics.

EPSRC CDT in Distributed Algorithms

This CDT delivers an innovative data science, AI and machine learning PhD programme. In collaboration with STFC Hartree and through strong partnerships with IBM Research and the Alan Turing Institute. It brings together diverse areas of expertise to train students in the skills and experience to ensure that they will have world-leading expertise to meet the need for highly-trained data scientists.

EPSRC and ESRC Centre for Doctoral Training in Risk and Uncertainty

With a wide range of high profile industry partners, both local and international, the CDT offers students a unique experience based on a 'cohort approach' to learning, with direct industrial involvement and exposure to a wide range of disciplines. The CDT is part of the University's Institute for Risk and Uncertainty.

Doctoral Network in Technologies for Healthy Ageing

This doctoral network is training the next generation of physical scientists and engineers to develop novel technologies and devices to address the challenges faced by older people and our clinical colleagues who work with them. It is structured around three healthy ageing challenges - prolonging independence, maintaining wellness and accelerating recovery.

Doctoral Network in AI for Future Digital Health

Directed at creating and maintaining a community of AI health care professionals that can apply the latest research within AI to Health Care. Each PhD project is co-created in collaboration with a health provider and/or a healthcare commercial partner. The network provides students with training, culminating in a PhD, in a collaborative environment.

ESRC Centre for Doctoral Training Data Analytics and Society

This CDT will provide postgraduate research and training across four Universities – Liverpool, Leeds, Manchester and Sheffield. It will encourage significant advances to bring together social science with methods from computing, mathematics and the natural sciences. Focussing on the creation and analysis of new longitudinal and streamed data resources for socio-economic investigations, creating new methods, investigating social processes and facilitating interventions.