Cranfield University

What you'll study

This programme combines numerical analysis, scientific computing and software engineering with practical training in high-performance computing and modern development tools. Core themes include numerical methods for partial differential equations, finite element and finite volume techniques, computational fluid dynamics (CFD) fundamentals, and algorithms for large-scale simulation. You will also study software engineering topics such as software architecture, version control, testing, verification and validation, and performance optimisation.

Typical taught modules cover:

• Numerical Methods and Mathematical Modelling — discretisation techniques, stability and convergence, solution of linear and nonlinear systems.
• Finite Element and Finite Volume Methods — formulation and implementation for structural and fluid problems.
• Computational Fluid Dynamics — governing equations, turbulence modelling, solvers and boundary treatments.
• High-Performance and Parallel Computing — MPI/OpenMP programming, GPU computing, scalability and profiling.
• Scientific Software Engineering — software design patterns for scientific applications, continuous integration, code review and testing strategies.
• Uncertainty Quantification and Optimisation — sensitivity analysis, probabilistic methods and optimisation for design under uncertainty.
• Data Assimilation and Machine Learning for Engineering — basic data-driven methods used alongside physics-based models.

The programme culminates in a substantial individual project or industrial research project where you apply computational techniques to a real engineering problem, producing a software deliverable and a thesis-level report.

Entry requirements

Applicants are normally expected to hold a good undergraduate degree in engineering, mathematics, physics, computer science or a closely related discipline. Typical degrees are a UK upper second-class honours (2:1) or international equivalent; applicants with a lower-second-class degree (2:2) plus relevant industrial experience may also be considered. Practical programming experience (for example in Python, C/C++ or Fortran) and a solid grounding in calculus and linear algebra are desirable.

International applicants whose first language is not English will need to demonstrate proficiency in English. Cranfield accepts a range of recognised qualifications; refer to the university's official guidance for the current accepted tests and minimum scores. Applicants should submit academic transcripts, a personal statement outlining relevant experience and motivation, and references. Some applicants may be asked for evidence of numerical or programming ability.

Career prospects

Graduates are well placed for roles that require a blend of numerical analysis and software skills. Common career paths include:

• Computational engineer or simulation engineer in sectors such as aerospace, automotive, energy and defence.
• Scientific or software developer building engineering simulation tools and solvers.
• CFD or structural analysis specialist working on product development and validation.
• High-performance computing engineer or performance optimisation specialist.
• R&D engineer in industrial research centres or technical consultancies, and progression to doctoral research for those pursuing academia.

Employers of graduates typically include engineering manufacturers, consultancy firms, national laboratories, software houses developing CAE/CFD tools and organisations running large-scale simulation infrastructure.

Why study at Cranfield University

Cranfield is a postgraduate-focused university with strong links to industry and a long history of applied engineering research and teaching. The programme is taught by academic staff with active research profiles in computational mechanics, CFD, optimisation and scientific software, and benefits from collaboration with Cranfield's research centres and industrial partners.

Students have access to high-performance computing facilities, dedicated computational laboratories and practical support for software development and code verification. The emphasis on an industry-relevant project gives hands-on experience solving real engineering problems and producing an engineering-quality software deliverable, helping to bridge the gap between academic training and professional practice.