The programme is specifically tailored to develop students from a strong mathematics background into becoming genuinely multidisciplinary scientists. You will have the opportunity to develop your mathematical and computational modelling skills, whilst at the same time being trained in cutting-edge interdisciplinary techniques, including the option of practical work. You will learn how to diversify your skills into other fields, and how to work with research leaders and other students from different disciplines.
Most things in the real world are complex and difficult to understand, from biological systems to the financial markets to industrial processes, but explaining them is essential to making progress in the modern world. Mathematical modelling is a fundamental tool in the challenge to understand many of these systems, and is an essential part of contemporary applied mathematics. By developing, analysing and interpreting mathematical and computational models we gain insight into these complex processes, as well as giving a framework in which to interpret experimental data. To fully capitalise on these tools, there is a fundamental need in both academic research and industry for a new generation of scientists trained to work at the interdisciplinary frontiers of mathematics and computation. These scientists require the ability to assimilate and understand information from other disciplines, communicate with and enthuse other researchers, as well as having the advanced mathematical and computational skills needed.
This course is tailored to train students for careers in scientific research, and for employment in a wide range of industrial contexts, for example biotechnology, industrial engineering or the pharmaceutical industry. There is a considerable need for scientists with a strong mathematical and computational background who can communicate with experimental scientists; this MSc will provide you with specialised training, and through your research skills project, evidence that you can work in this multidisciplinary context.