UK to train 100 PhD students in data science
A new PhD programme to develop techniques to handle the vast amounts of data being generated by experiments and facilities has been launched by the UK’s Science and Technology Facilities Council (STFC). Around 100 PhD students will be trained, at a cost of £10m, to analyse data in areas such as astrophysics, accelerator science, nuclear and particle physics, as well as tackle problems posed by industry.
Data scientists use computer, statistical and programming techniques to extract information from large datasets – many of which would take years to analyse manually. Modern observational and experimental facilities allow researchers to gather vast amounts of data, but many more specialists are needed, the STFC says, to make sense of it and help make new discoveries. The STFC adds that the cash will be used to train students in data-intensive science through cutting-edge research projects and targeted academic-training programmes, complemented by secondments to national and international partners.
Most of the funding comes from a £90m investment for 1000 new PhD places, mainly in science, technology, engineering and mathematics, that was announced earlier this year by the UK government. The PhD data students will be trained at eight new Centres for Doctoral Training (CDT) in data-intensive science based at 19 UK universities, which include Bristol, Cambridge, Swansea, Sussex and Queen Mary.
“This investment will not only bring on the next generation of much-needed data scientists with the skills and knowledge to become leaders in the field, it will be crucial in ensuring the UK research sector and the UK economy remains competitive on the world stage,” says Grahame Blair, the STFC’s executive director of programmes, adding that the investment will build on the big data challenges facing physics.
Stephen Fairhurst, a gravitational-wave physicist at Cardiff University who is director of its Data Innovation Research Institute, says that the CDTs are exciting as they will give students training in cutting-edge big-data methods to apply to both research problems in physics and astronomy, and to real-world problems.