UKRI CDT Scholarships at Swansea University in AI, Machine Learning and Advanced Computing

  • Phd
  • Full cost of UK/EU tuition fees, plus a stipend
  • 31 January 2020

Various subject areas: UKRI Centre for Doctoral Training Scholarships in Artificial Intelligence, Machine Learning and Advanced Computing

The scholarships are funded by UK Research and Innovation (UKRI).

Subject areas: Physics and astronomy; biological and health sciences; mathematics and computer science

The UKRI CDT Artificial Intelligence, Machine Learning and Advanced Computing provides 4-year, fully funded PhD opportunities across broad research themes:

  • T1: data from large science facilities (particle physics, astronomy, cosmology)
  • T2: biological, health and clinical sciences (medical imaging, electronic health records, bioinformatics)
  • T3: novel mathematical, physical, and computer science approaches (data, hardware, software, algorithms)

Its partner institutions are Swansea University (lead institution), Aberystwyth University, Bangor University, University of Bristol and Cardiff University.

Training in AI, high-performance computing (HPC) and high-performance data analytics (HPDA) plays an essential role, as does engagement with external partners, which include large international companies, locally based start-ups and SMEs, and government and Research Council partners. Training will be delivered via cohort activities across the partner institutions.

Positions are funded for 4 years, including 6-month placements with the external partners. The CDT will recruit 11 positions in 2020.

The partners include We Predict, Intel, ATOS, DSTL, GCHQ, IBM, Microsoft, Quantum Foundry, Dwr Cymru, Amplyfi, DiRAC, agxio, NVIDIA, Oracle, QinetiQ, TWI and many more.

More information, and a description of research projects can be found at the UKRI CDT Artificial Intelligence, Machine Learning and Advanced Computing website.

The CDT is built upon longstanding research and training collaborations between the universities of Aberystwyth, Bangor, Bristol, Cardiff and Swansea. In addition, Supercomputing Wales and the University Computing Academies provide bespoke support via Research Software Engineers and access to HPC facilities in a coordinated fashion. Meet the Staff

Fully-funded PhD positions are available for students with a strong interest and aptitude in computational science and in one of our research themes. Positions are funded for 4 years, including the placements with the external partners.

The programme consists of a substantial training component in the first year, including cohort-based training in AI and computational methods, to establish a common base. Engagement with our external partners is embedded throughout and includes short-term placements in Year 1 and 2 and a 6-month placement in Year 3/4. Transferable skills training is delivered via residential meetings, at our annual CDT conference, and in cooperation with the Alan Turing Institute. More details can be found on the Training page.

Swansea University
This is a scholarship by

Swansea University

Swansea, is a coastal city and county, officially known as the City and County of Swansea in Wales.

Eligibility

The typical academic requirement is a minimum of a 2:1 undergraduate degree in physics and astronomy; biological and health sciences; mathematics and computer science or a relevant discipline. However, each partner institution has a different requirement.

Candidates should be interested in AI and big data challenges, and in (at least) one of the three research themes. You should have an aptitude and ability in computational thinking and methods (as evidenced by a degree in physics and astronomy, medical science, computer science, or mathematics, for instance) including the ability to write software.

Due to funding restrictions this scholarship is open to UK/EU candidates only.

Benefits

Each scholarship covers the full cost of UK/EU tuition fees and an annual stipend.

Additional funding is available for training, research and conference expenses.

Application

Please visit our website for more information.

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