4 courses offered in the Institute of Astronomy
Astronomy - MPhil
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.
Astronomy - PhD
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.
Astrophysics - MASt
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.
The MASt in Astrophysics consists of: a research project which accounts for a third of the total marks available for the course, and comprises of a written report (85%) and a formal oral presentation (15%) to the Part III/MASt Examiners; and a choice of a range of high level specialist courses, most of which are examined in June.
Planetary Science and Life in the Universe - MPhil
The MPhil in Planetary Sciences and Life in the Universe is a 10-month cross-departmental programme designed to deliver outstanding postgraduate level training in the search for life’s origins on Earth and its discovery on planets beyond Earth.
The course structure has been designed by leading scientists to provide students with the theoretical knowledge, practical experience, and transferable skills required to undertake world-leading research in Planetary Sciences and Life in the Universe. Graduating students will be equipped with the discipline specific-specialisations and skills of a masters course, whilst gaining understanding in how the core areas that bridge PSLU fields form the cross-disciplinary foundation of this exciting new frontier.
Graduates of the course will gain valuable skills rooted in the study of the physics, chemistry, mathematics, and biology of planetary science and life in the universe. Transferrable skills training is delivered through the three group-based projects running over the year: these provide a unique opportunity for students to gain experience of leadership, collaboration, and written and oral communication. The training provided will be an outstanding foundation for PhD research in planetary science, exoplanetary science, Earth system science, planetary astrophysics, astrobiology and allied disciplines, or for the wide range of careers where analytical skills, excellent communication, and experience of leading collaborations are key.
1 course also advertised in the Institute of Astronomy
Data Intensive Science - MPhil
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. The course covers material from the fields of machine learning and AI, statistical data analysis, research and high performance computing, and the application of these topics to scientific research frontiers.
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.
This course will equip students with all the skills required for modern scientific data analysis, enabling them to participate in large experimental or observational programmes using the latest statistical and machine learning tools deployed on leading-edge computer architectures. These computational and statistical skills will also be directly applicable to data-driven problem-solving in industry.