The centre will be expected to engage the broader user and employer community, including industry (especially small and medium enterprises) and other relevant organisations.
These organisations should have active engagement in determining and providing input to the training programme and in mentoring and co-supervising students, with additional input and guidance from STFC and UKRI where appropriate.
Students will be expected to undertake an original research project, which brings together big data skills and expertise from STFC’s remit:
- accelerator physics
- solar and planetary science
- particle physics
- particle astrophysics and cosmology
- nuclear physics.
They must apply the expertise to a different sector or industrial context during their placement. Projects should demonstrate their relationship to other STFC and UKRI investments in data intensive science and artificial intelligence (AI) where appropriate.
The non-STFC funded students will similarly be expected to undertake original research in data intensive projects. These would normally fall within STFC’s remit, but could on exception fall outside. However the training programme for the whole cohort of students must be coherent and add considerable value.
The centre will be required to provide a structured cohort-based training programme for the students, particularly in their first year, in which students undertake a formal, accessible programme of taught coursework. This should be specifically designed to give them a broad and thorough grounding in computational techniques and other issues relating to big data challenges.
At least six months of each studentship must be spent outside the centre in one or more private, public (including national or international facilities) or third sector organisations engaged in the development or use of data intensive science techniques.
These placements may be undertaken in one block or split into two or more shorter periods of a minimum of three months each. They should be designed to enable students to gain additional expertise in data intensive science and develop a broader understanding on the wider uses of data intensive techniques and their application.
While the skills and experience gained by the student will be beneficial to their PhD research, their time spent on placement should not be an integral part of their thesis work.
In addition, all students will be expected to access the general training opportunities required for accreditation and to enhance their understanding of the innovation process including working with industrial partners as appropriate.
The centre’s training activities should also develop and enhance interdisciplinary technical knowledge and demonstrate their relationship to other STFC and UKRI investments in data intensive science and AI.
The centre will need to have an appropriate management structure, expected to comprise a director and senior management team, with independent strategic oversight which must include representation from the non-academic sector and from STFC and UKRI.