The Applied Mathematics group in the Department of Mathematics at the University of Manchester has a long-standing international reputation for its research. Expertise in the group encompasses a broad range of topics, including Continuum Mechanics, Analysis & Dynamical Systems, Industrial & Applied Mathematics, Inverse Problems, and Numerical Analysis & Scientific Computing. The group has a strongly interdisciplinary research ethos, which it pursues in areas such as Mathematics in the Life Sciences, Uncertainty Quantification & Data Science, and within the Manchester Centre for Nonlinear Dynamics.
The Applied Mathematics group offers the MSc in Applied Mathematics as an entry point to graduate study. The MSc consists of five core modules (total 75 credits) covering the main areas of mathematical techniques, modelling and computing skills necessary to become a modern applied mathematician. Students then choose three options, from a list including specialist options relevant to numerical analysis and industrial modelling (total 45 credits). Finally, a dissertation (60 credits) is undertaken with supervision from a member of staff in the applied mathematics group with the possibility of co-supervision with an industrial sponsor.
The selection of optional courses in the MSc are centred around the numerical analysis and industrial mathematics, reflecting research strengths within the Applied Mathematics group at Manchester.
Numerical Analysis - the study of algorithms for the problems of continuous mathematics - has been an area of strength since the first stored-program electronic digital computer, the Baby, was born at the University of Manchester in 1948, and we have run an MSc course in numerical analysis continuously since 1959. The optional numerical analysis modules develop essential skills for analysing, designing and implementing mathematical algorithms for leading-edge scientific computing.
Industrial Mathematics and Industrial Modelling (any aspect of mathematics that can influence the way industry approaches or solves problems) is having increasing importance within a variety of industrial sectors. Typical examples of industrial modelling problems are modifications to the way that fluid is pumped through a pipe, the design of algorithms for data encryption, modelling new types of materials used for sound reduction, understanding the instability between fluids of different viscosities, and determining how soft tissue deforms under applied forces.