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Postgraduate Study

3 courses offered in the Institute of Astronomy

The assessment for the Astronomy MPhil degree is exclusively by research and a project and supervisor must have been identified prior to a formal application being made by the student. There is no taught element.

The MPhil degree is not suitable for physicists and mathematicians wishing to prepare for a research PhD in Astrophysics and the number of students admitted is small.

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The Institute of Astronomy offers the opportunity to study for the PhD degree. Projects may be exclusively theoretical or observational but many combine aspects of both.  Many projects incorporate aspects of Data Science including machine learning and artificial intelligence.

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The Institute of Astronomy offers an exciting opportunity for suitably qualified students who have completed a bachelor's degree (or equivalent) in astronomy, physics or mathematics to study for a one-year master level qualification in astrophysics.

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1 course also advertised in the Institute of Astronomy

From the Department of Physics

The MPhil in Data Intensive Science is a 10-month cross-departmental programme in the School of the Physical Sciences which aims to provide education of the highest quality at the master’s level. The programme covers the full range of skills required for modern data-driven science from the fields of machine learning and AI, statistical data analysis, and research computing.  

The course structure has been designed in collaboration with our leading researchers and industrial partners to provide students with the theoretical knowledge, practical experience, and transferable skills required to undertake world-leading data-intensive scientific research. Students will gain the broad set of skills required for scientific data analysis, covering traditional statistical techniques as well as modern machine learning approaches.  Both the theoretical underpinnings and practical implementation of these techniques will be taught, with the later aspect including training on software development best practice and the principles of Open Science. The course also aims to provide students with direct experience applying these methods to current research problems in specific scientific fields. Students who have completed the course will be equipped to undertake research on data-intensive scientific projects. Beyond academic disciplines, students will be well prepared for a career as a data science professional in a broad range of commercial sectors.

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Department Members

Professor RG McMahon

  • 17 Academic Staff
  • 60 Postdoctoral Researchers
  • 58 Graduate Students
  • 30 Undergraduates

Research Areas