Applied Computational Science & Engineering MSc
Inspiring the future crop of experts in Computational Science and Engineering
Students will gain deep knowledge and skills in cutting-edge computational techniques for real world science and engineering applications to meet industry demand.
- See how our Appplied Computational Science and Engineering MSc students took their Machine Learning Module
- Find more about student applications on our COVID-19 webpage
The Applied Computational Science and Engineering MSc will educate future domain-specialists in computational science. This course will expand your knowledge of numerical methods, computational science, and how to solve large scale problems by applying novel science and engineering approaches. Graduates of this course will fill the market demand for those with applied, hands-on computational experience who can solve real world problems.
Who should apply for the MSc?
The MSc is suitable for graduates of disciplines including mathematics and physical sciences, geophysics and engineering, and computer science. All students should have undertaken some programming in a high-level language (including Matlab, Python or C/C++).
Dr Gerard Gorman, Course Director, Applied Computational Science & Engineering MSc: “Students will learn to solve real-world problems using numerical methods and computational science for a broad range of applications in science and engineering”
Dr Adriana Paluszny, Royal Society University Research Fellow: “Students will engage with a broad range of trending coding techniques and applications – we will prepare them for success in industry and academia”
Students will have the chance to participate in individual and group research projects as well as to write reports and present technical work, developing the project management and numerical skills that are desired by employers.
The study programme consists of eight taught modules, and one individual research project which accounts for one third of the study programme.
Modern programming methods
Modelling dynamical processes
Applying computational science
Patterns for parallel programming
Inversion and optimisation
- Computational fluid dynamics for turbulent flows
- Modelling of wind turbines and their wakes
- Multi-scale tsunami inundation and sea defence modelling
- Adjoint based inversion and optimisation methods
- Interface construction for multimaterial flow modelling in 3D
- Modelling asteroid impact processes
- Generative Adversarial Networks for generating geological models
- Deep Learning applied to the interpretation of subsurface data
A numerical study of the interaction between hydraulic fractures and natural fractures
Computational Methods in Radiation Transport Using Supercomputers
Coupled multiphase SPH-DEM simulations of an aerated fluidized bed
Deep Learning for Solving PDEs
Multi-material Interface Tracking in a Three-Dimensional Shock Physics Code
Offshore wind farm wake modelling using deep feed forward neural networks for active yaw control and layout optimisation
Predicting fracture growth on Jupiter’s icy satellite Europa using finite element modelling
The Space Filling Curve Convolutional Neural Network for use with Multi-Dimensional Unstructured Finite Element or Control Volume Meshes
Traffic congestion recognition based on remote sensing images and machine learning
Graduates of this course will fill the market demand for those with applied, hands-on computational experience who can solve real world problems.
Through the combination of programming, foundational domain knowledge and advanced numerical literacy that this course provides, graduates will be highly sought after to work as expert analysts in industry, for example, in geoscience, risk management or climate science. Graduates will be in an ideal position to pursue academic careers in fields such as computational techniques, optimisation and inversion, fluid mechanics, and machine learning applications.
This immersive, hands-on MSc course will enable students to develop their skills and techniques for a range of science and engineering applications utilising High Performance Computing resources. Students will learn alongside world-class researchers in the Department of Earth Science and Engineering. There will be a strong emphasis on high productivity problem solving using modern computational methods and technologies, including computer code development and parallel algorithms.
Applicants who want to pursue analytical careers in industries across science and engineering are a target for this course. Graduates will develop the skills necessary to enter the modern industrial workforce. This MSc will also prepare for your PhD studies in fields such as computational techniques, simulation, numerical modelling, optimisation and inversion, heat transfer, and machine learning applications.
The Applied Computational Science and Engineering MSc programme will ensure that students are able to apply appropriate computational techniques to understand, define and develop solutions to a range of science and engineering problems. You will have the chance to participate in individual and group research projects as well as to write reports and present technical work, developing the project management and numerical skills desired by employers.
Teaching staff for the Applied Computational Science and Engineering MSc include: Dr Gareth Collins, Prof Olivier Dubrule, Dr Saskia Goes, Dr Gerard Gorman (Course Director), Prof Joanna Morgan, Prof Stephen Neethling, Dr Adriana Paluszny, Prof Matthew Piggott, Prof Michael Warner
- Find out more about postgraduate study at Imperial College London, including tuition fees, admissions and how to apply.
- See frequently asked questions about the MSc
- Read case studies of the Department's Computational Geosciences research.
- Read about how the MSc will benefit from innovative software deployed at Imperial College London.
Open Day Presentation
Please take a look at a brief overview of the MSc in the 2019 Open Day ACSE Presentation.
Contact: With any queries about the course, please email Education Administrator, Ying Ashton.
For more information about multi-mode delivery, your learning experience and the steps we’ll be taking to keep you safe on campus please see our Covid 19 information for applicants and offer holders page or contact your course administrator, Ying Ashton.