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Artificial Intelligence and Machine Learning in Science at Queen Mary University of London - UCAS

Course options

2025 - 2026
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Course summary

Introduction Artificial Intelligence and Machine learning have become a revolutionary force in science and medicine, contributing to numerous important breakthroughs in recent years. From climate change to drug discovery to particle physics, we are witnessing unprecedented advances in automation, data analysis, predictive modelling, and simulations. This cross-disciplinary masters programme will teach you the fundamentals of AI and Machine Learning, and how these can be applied to real-world scientific problems. Programme highlights

  • Develop the coding and programming skills required to apply artificial intelligence to a wide range of fields.
  • Work with real datasets from our world-leading research with links to organisations such as CERN, NASA and LIGO.
  • Study the essential mathematical concepts that underpin artificial intelligence and machine learning such as probability, statistics and time series.
  • Learn from expert academics, including former industry practitioners, Fellows of the Alan Turing Institute and members of Queen Mary’s Digital Environment Research Institute (DERI).
  • No programming experience required.
Career outcomes The demand for qualified AI and Machine Learning experts is growing and we are witnessing an explosion of AI opportunities in London. From environmental science to healthcare, organisations are seeking skilled graduates with hands-on coding and programming skills. Given the broad scope of this MSc and the focus on developing strong theoretical and practical skills, graduates may wish to consider other sectors such as communications, finance or retail. Highly sought-after Machine Learning Engineers can earn over £60,000 per year in the UK (Source: Indeed).

Modules

Core Modules Probability and Statistics Machine and Deep Learning Research Methods Research Project in Data Science Elective Modules Bayesian Statistics Time Series Analysis for Business Graphs and Networks AI in Astrophysics and Space Science Machine Learning in Materials Discovery Cloud Computing in AI

Assessment method

Taught modules are assessed through a combination of coursework and written examinations. You will also be assessed through an individual research project.

How to apply

International applicants

Learn more about Visa and English Language requirements on our website.

Entry requirements

A good 2:2 or above at undergraduate level in Mathematics, Physics, Chemistry, Computing, Engineering or any other STEM subjects.

Fees and funding

Tuition fees

No fee information has been provided for this course

Tuition fee status depends on a number of criteria and varies according to where in the UK you will study. For further guidance on the criteria for home or overseas tuition fees, please refer to the UKCISA website .

Additional fee information

Students enrolling on a postgraduate degree programme are charged tuition fees each year by Queen Mary. The rate you will be charged depends on whether you are assessed as a Home/EU or Overseas student. You can find tuition fees for each course on the course finder pages on our website by clicking the apply link, or navigate here: https://www.qmul.ac.uk/postgraduate/ Further details about postgraduate taught tuition fees can also be found on our website: https://www.qmul.ac.uk/postgraduate/taught/tuitionfees/

Sponsorship information

Please visit our website to learn more about our scholarships and funding support.

Artificial Intelligence and Machine Learning in Science at Queen Mary University of London - UCAS