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Discover how numerical methods and computational science can help solve large scale problems

Work under the guidance of world-class researchers and use high-performance computing resources

Participate in individual and group research projects and enhance your project management and numerical skills

Course key facts

• Qualification

  • MSc

• Duration

  1 year

• Start date

  September 2025

• Study mode

  Full-time

• • £23,400 Home

  • £43,800 Overseas

• Delivered by

  • Department of Earth Science and Engineering

• Location

  • South Kensington

Apply novel science and engineering approaches to solve large scale problems on this Master's course, designed to advance your knowledge of numerical methods and computational science.

You'll develop skills and techniques for a range of science and engineering applications utilising high performance computing resources, with guidance from world-class researchers.

This course is part of the Ada Lovelace Academy, an initiative from the Department of Earth Science and Engineering aiming to deliver gender-balanced post-graduate education in computational subjects to solve the science and engineering challenges of the 21st century.

Structure

This page is updated regularly to reflect the latest version of the curriculum. However, this information is subject to change.

Find out more about potential course changes .

Please note: it may not always be possible to take specific combinations of modules due to timetabling conflicts. For confirmation, please check with the relevant department.

Learn how to produce and validate software for research and carry out test-driven software development and continuous integration.

Computational Mathematics

Explore the essential mathematics underpinning computational science, data science and machine learning.

Applying Computational/Data Science

Work collaboratively to solve problems using software and summarise your work using presentations.

Data Science and Machine Learning

Analyse concepts central to a data science approach and different types of classical machine learning algorithms.

Deep Learning

Discover how to build neural networks to recognise images and language, generate new content, and make predictions by learning complex patterns from data.

Modelling and Numerical Methods

Become familiar with the key concepts and algorithms required to represent the continuous world on discrete computers.

Advanced Programming

Master compiled programming in C++, including memory management, object-oriented design, and performance optimisation for building efficient and robust software.

Inversion and Optimisation

Examine the methods that can be used to extract useful information from incomplete, inconsistent and inaccurate physical datasets using practical computational resources.

Patterns for Parallel Programming

Explore basic concepts of parallel programming and learn implementation strategies based on shared-memory parallelism and the message-passing paradigm.

You'll conduct an independent analysis of a technical problem.

The process will see you devise, implement, test, modify, validate and document a computational or data science-based solution.

You can opt for a problem provided to you, or solve a problem of your own devising. Computational projects as part of external industry placements will also be considered.

You'll present your work in a written report and an oral presentation.