Our MSc delivers an outstanding combination of advanced application-oriented mathematical concepts and computational methodologies. Broad in scope and genuinely multidisciplinary in nature, it is based on solid and well-founded mathematical theory.
Subject modules are carefully designed to be accessible to anyone with a good first degree in mathematics or in science and engineering subjects which have a strong mathematical component.
You’ll normally need a 2:1 bachelors degree or above in mathematics or a closely related discipline with significant mathematical content on an appropriate level, or equivalent, or significant work experience in a management or supervisory position.
This course is made up of three stages – Postgraduate Certificate, Postgraduate Diploma and MSc.
You will study these modules:
This module develops learners’ knowledge, understanding and ability to implement a diverse range of optimisation techniques, which reflect current research activity. Many problems that cannot be solved by classical methods can be investigated and solved using modern optimisation techniques. The theoretical background, key results and applications are presented through numerous examples. Optimisation techniques are implemented using relevant Mathematical Software.
The aim of this module is to develop the reflective and practical understanding of computational techniques in Mathematics. The course will enable students to obtain an insight into the main computational techniques and its practical implementation to address relevant mathematical models and their applications. A variety of numerical methods and techniques will be discussed, including numerical solutions of linear systems, numerical integrations, as well as ordinary and partial differential equations.
The module builds upon fundamental knowledge of elementary probability concepts and develops the foundations for modelling random phenomena mathematically, in particular the mathematical description of systems’ time evolution by stochastic processes. Based on an introduction to the general concepts of stochastic processes, the major focus is on Markov chains. Analytical, numerical and simulative methods for analyzing Markov models arising in application domains are presented.
You will study these modules:
Networks and Algorithms
The module develops knowledge and understanding of challenging topics in Graph Theory, Networks and Algorithms. Relevant and up-to-date applications in simulation of complex networks, for example social networks and computer networks, will give the students skills for today´s working environment in an increasingly complex networked world. The topics chosen lead to or reflect current research activity and give a solid basis for further study on higher levels.
Non-linear System Dynamics
The overall aim of the module is to enhance skills in the analysis and applications of non-linear ordinary differential equations (ODEs). Such equations are relevant to a wide range of physical systems, and the subject area is an important one in the context of system modelling. The techniques studied are both quantitative and qualitative in nature, and the module examines different types of non-linear equations and techniques for both their analysis and solution.
The students will be exposed to a variety of theoretical and applicable aspects of topics through a combination of formal lectures and tutorial sessions. Any computational requirements will be met by existing software resources within the School as a means to facilitate problem solving and consolidate understanding of subject matter. Computing facilities support the use of numerical/symbolic programming languages.
Studying at Masters Level & Research Methods
The module aims to develop your ability to study at Master’s level and to develop, plan, and execute a project using the processes of research. This module is a prerequisite for undertaking the Master’s dissertation (Independent Scholarship).
You will study this module:
This module provides the opportunity for students to consolidate upon and extend their understanding, skills and knowledge of their subject area as developed through stages 1 and of the programme. Through this module students will demonstrate their knowledge, understanding and skills at the Master’s level. The aim is to ensure that students are able to formulate and tackle research questions competently, efficiently, independently, and with relevance to a particular problem and/or application.
In our increasingly complex networked world – where innovative technologies are constantly emerging – the skills you will develop on this MSc programme are particularly valuable.
Computational mathematics is relevant to almost every science, engineering, business and finance discipline as well as many industrial sectors. With this qualification, you will be able to communicate your knowledge to professionals from diverse backgrounds and operate within – and lead – multidisciplinary teams.
Studying for the MSc is also an excellent preparation for academic research in any area where computational techniques play a significant role, offering a clear route to further study at PhD level.
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