Our CDT aims to provide a training environment in which doctoral researchers working on a research topic in, say, health informatics, particle physics or deep learning, will be able to exchange interdisciplinary methods and tools, and hence dramatically enhance the research environment. This will be achieved employing a mixture of in-person and virtual meetings, mentoring schemes across the cohorts and institutions, and the co-development of projects, including peer-to-peer learning.
Some key components are:
- Taught components in Year 1, consisting of a selection of foundational modules in AI and computing, to establish a common knowledge base, and optional modules, specific to the research theme and the institution.
- Computing and data specific skills, developed in a hands-on fashion, via the Software Carpentry and an extended coding challenge.
- Residential meetings, to further develop a coherent community across the institutions. The meetings will include scientific presentations, transferable skills training and engagement with external partners.
- Responsible innovation, to link creativity and opportunities in science and innovation with a sense of social responsibility.
- Placements at external partners, woven throughout the four-year programme. This includes a six-month placement in the second half of the PhD.
The training programme will be further developed in cooperation with the external partners and CDT students, acting on feedback and ongoing interaction.