Academic requirements/experience Minimum second-class (2:2) Honours degree or overseas equivalent* in mathematics, computer science or a closely related discipline Prospective students with relevant experience or appropriate professional qualifications are also welcome to apply. *For Australia and Canada, normal degrees are accepted. Don’t meet the academic entry requirements? We offer the Pre-Masters Preparation Course at the University of Strathclyde International Study Centre, for international students (non-UK/Ireland). English language requirements You must have an English language minimum score of IELTS 6.0 (with no component below 5.5). We offer comprehensive English language courses for students whose IELTS scores are below 6.0. Please see ELTD for full details . As a university, we now accept many more English language tests in addition to IELTS for overseas applicants, for example, TOEFL and PTE Cambridge. View the full list of accepted English language tests here .
Our MSc Advanced Computational Mathematics is an advanced course, offering the opportunity to develop understanding of computational mathematics and numerical analysis, enabling students to model complex real-world problems. The course combines theoretical foundations with practical computing skills, making students highly employable in finance, technology and scientific computing.
You will have the opportunity to gain skills in:
In addition to compulsory modules, there are a range of elective modules, meaning you can tailor the course in line with your career interests.
Semester 1 In Semester 1, modules will focus on a strong foundation in computational techniques, including finite element methods for boundary value problems and machine learning mathematics. These modules will equip you with the theoretical and practical skills needed for advanced numerical computing.
Semester 2 In Semester 2, the focus shifts to numerical methods and deep learning for partial differential equations, expanding your understanding of algorithmic approaches to complex mathematical problems.
Throughout both of these semesters, you will also have the opportunity to choose from a selection of optional modules, allowing you to tailor the course to your interests in areas such as optimisation, big data, and financial mathematics.
Semester 3 Semester 3 is dedicated to a research project, where you will apply your computational mathematics skills to tackle a real-world problem. This project is an excellent opportunity to develop your independent research abilities and engage with industrial or academic applications.
Classes are delivered by a number of teaching methods:
Teaching is student-focused, with students encouraged to take responsibility for their own learning and development. Classes are supported by web-based materials.
The form of assessment varies from class to class. For most classes the assessment involves both coursework and practical computer-based or written examinations.
The Department of Mathematics & Statistics has teaching rooms which provide you with access to modern teaching equipment and University computing laboratories, with all necessary software available.
You'll also have access to a common room facility which gives you a modern and flexible area for individual and group study work and is also a relaxing social space.
At the heart of the Department of Mathematics & Statistics is the University’s aim of developing useful learning. We're an applied department with many links to industry and government. Most of the academic staff teaching on this course hold joint-appointments with, or are funded by, other organisations, including APHA, Public Health and Intelligence (Health Protection Scotland), NHS Greater Glasgow and Clyde and the Marine Alliance for Science and Technology Scotland (MASTS). We bridge the gap between academia and real-life. Our research has societal impact.
Though the programme involves all staff in the Mathematics and Statistics department, it is primarily led by three level-2 Research Groups, namely Numerical Analysis, Continuum Mechanics & Industrial Mathematics, and Data Scienc