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Centre for Doctoral Training in Data Intensive Science

19 Nov 2017

UCL's Centre for Doctoral Training in Data Intensive Science

UCL was selected, after a highly competitive process, to host STFC's first Centre for Doctoral Training (CDT) in data intensive science (DIS). DIS encompasses a wide range of areas in the field of 'big-data' including the collection, storage and analysis of large datasets, as well as the use of complex models, algorithms and machine learning techniques to interpret the data. The Centre will primarily carry out research in STFC's flagship Data Intensive Science projects, in High Energy Physics and Astronomy, which have been at the forefront of DIS research for several decades and provide the ideal training ground for DIS.

Over 80 academics from across UCL will be involved in the Centre. This includes academics from the High Energy Physics, Astrophysics and the Atomic and Molecular Physics groups in the Department of Physics & Astronomy, as well as academics from the departments of Space and Climate Physics, Computer Science, Mathematics, Electrical Engineering and Statistical Science. The CDT's vision is to provide a unique studentship experience, that will produce highly trained and employable PhD graduates with advanced and widely applicable skills in DIS, who will ultimately become the future leaders of this field in both academia and industry. In addition, by bringing together DIS experts from a range of sectors and fields, it will promote the development of new DIS techniques or the application of existing techniques to new areas, which will have a wide and significant impact on all sectors.

The Centre launched in September 2017 and is now hiring for its 2nd cohort, who will start their PhDs in September 2018.

For more information on the CDT, please contact: Prof. Nikos Konstantinidis or Prof. Ofer Lahav

Over 80 academics from 5 departments are involved in the Centre, incorporating a wide-range of expertise in both applied and theoretical DIS. All projects will have an academic from another department on the supervisory teams.