At the CSC, we offer training in scientific computing by a taught masters course. This course is largely lecture based, but also involves a dissertation component. Opportunities exist for entry in October each year. Applications are welcomed for Research Council funded projects and from self-funding students.

Applicants should possess, or hope to obtain, a 1st-class honours degree in one of the following sciences: Biology, Chemistry, Computer Science, Mathematics, Physics, Statistics. Applicants from closely related disciplines will also be considered.

Some feedback from prospective employers:

We have recently had a student who did the MSc .. completing his 
PhD and we would like more students who have come through this 
type of route.

Thanks,
Kenneth J. Badcock, 
Professor of Computational Aerodynamics, University of Liverpool

For some past and current student profiles click here.

The course is organised into 180 CATS worth of modules. All students must take the core Scientific Computing modules which are:

Foundations of Scientific Computing This is a pre-sessional course which can be completed online before arriving or in the first week of the course. It covers the essential material assumed by later courses. The course is intended as revision and carries no credit for the MSc although it must be completed by all students.

High Performance Scientific Computing (18 CATS) Covering the basic tools needed by all researchers working in Scientific Computing. This included parallel computing, both OpenMP and MPI, programme development and visualisation. This course is assessed by course work and is aimed at developing real, practical skill.

Computational Linear Algebra and Optimization (18 CATS) Everyone who works in Scientific Computing will eventually have to deal with problems in Linear Algebra. For many it is their core activity. This course introduces the basic theory and algorithms required to make progress on such problems. As well as taught material there are small projects to help students get experience of using these techniques on real problems.

Data Structures and Algorithms and Algorithm Design (12 CATS) A training in the rigours of computer science can be of great benefit to scientific computing. This module covers material which is of value to all scientist and engineers and can be used as an introduction to more advanced course taught by computer science.

Monte Carlo Methods (18 CATS) Statistical techniques are core to many aspects of scientific computing ranging from quantum chemistry to financial market modelling. This course covers the basic theory of Monte Carlo methods and is examined by both examination and project work.

Computational PDEs (Numerics) (12 CATS) Partial differential equations are the language of fluid dynamics from astrophysical shocks to aeroplane design. This course introduces the techniques available for the numerical solution of these equations either through grid based or spectral techniques.  

The core programme has a combined CATS load of 78. Each student on the MSc is assigned an academic supervisor. This supervisor will advise the student on the choice of other modules (12 CATS worth) which will complete the CAT load for the taught component of the course. These modules can be chosen from any department provided they are at the required level. It is intended that these courses will reinforce the scientific interests of the students and can be from any participating department, e.g. physics, mathematics, statistics, chemistry, engineering, biology, systems biology, horticultural research or computer science. 

The remaining 90 CATS of the MSc, i.e. 50% of the marks, will be based on a dissertation which will begin in July and run until September, although some preparatory work must be completed in terms two and three. This will be in any area of Scientific Computing and the details of this will be agreed between the student and their supervisor.

Students on the MSc programme will have access to Linux desktop machines for routine work. CSC manages, and makes available to its MSc students, high perfomance computing facilities, an SMP machine and large commodity cluster, which MSc students can use to learn parallel programming and in their project work.  

Please consult our Applications page for details of the application procedure and links to the on-line application forms. Information on course fees and funding opportunities is available at the Fees and Financial Assistance page.

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