All Engineering Mathematics degree programmes are built on four key themes which cover theoretical and practical aspects of the application of mathematics. The themes run throughout the entire degree programme, developing your technical skills in each area as you progress, reaching the frontiers of current research in the fourth year of our MEng degrees:
The flagship theme of the Engineering Mathematics degree programmes. You'll apply all the skills from the rest of the programme to solve real world problems, working together or in groups. The case study problems are up-to-the-minute, span a huge range of discipline areas, and are derived from current research, industry and business.
Core mathematics is an inherent part of our degree programmes, and the type of mathematics you'll study is incredibly varied. In first year, the mathematics units bridge the gap between school and university mathematics, while in the final year many units are motivated by our research interests, and will take you to the frontiers of the subject. You'll learn a unique blend of continuous and discrete mathematics, enabling you to understand the underlying structure of an extraordinarily wide range of applications.
Computing is crucial for applying mathematics in the real-world. Unlike in text books, many problems at the cutting edge of science and engineering do not result in equations with nice analytical solutions. This means using computers to find approximate numerical solutions. We'll teach you the programming skills needed to complement your other technical skills.
You will gain an excellent background in general engineering so that you can really engage with practical problems, as well as learning the language of engineering. You'll have the chance to study courses across the Faculty of Engineering, including dynamics and control, thermodynamics, fluids, space systems, cryptography and sustainability.
Data science and machine learning
Being able to analyse big data is nowadays a fundamental skill. We will provide you with strong statistical foundations and specialistic knowledge of the most effective machine learning techniques to address the challenges of data analysis across scales and applications.