Course summary
The Complex Systems Modelling – from Biomedical & Natural to Economic & Social Sciences MSc course will teach you to apply mathematical techniques in the rapidly developing and exciting interdisciplinary field of complex systems and examine how they apply to a variety of areas including biomedicine, nature, economics and social sciences. This research-led course is suitable for graduates who wish to work in research and development in an academic or industrial environment. Key benefits
- Located in the heart of London, giving unparalleled access to research facilities.
- You will be studying innovative modules covering modern theories of complex systems modelling.
- Research-led study course taught by staff who are recognised leaders in their field.
Entry requirements
Bachelors degree with 2:1 honours degree in a suitable quantitative discipline, such as mathematics, physics, computer science, or engineering. A sound background in basic mathematics, in particular a familiarity with standard concepts of calculus, linear algebra, differential equations and elementary probability theory, will be assumed. In order to meet the academic entry requirements for this programme you should have a minimum 2:1 undergraduate degree with a final mark of at least 60% or above in the UK marking scheme. If you are still studying you should be achieving an average of at least 60% or above in the UK marking scheme. A 2:2 honours degree (or international equivalent) may be acceptable depending on the candidate's academic background.
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
Provider information
King's College London, University of London
Strand
Westminster
WC2R 2LS
Course contact details
Visit our course pageKing's Admissions Office
+44 (0) 20 7123 4843