Applied Computational Science & Engineering MSc
Duration: 1 year full-time
Start Date: October 2021
Campus: South Kensington, London
ECTS: 90 Credits
If you are unable to pay a course’s application fee, we encourage you to apply for a fee waiver. If you are interested in this MSc and wish to apply for a fee waiver, please contact email@example.com prior to starting your application.
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.
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- 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.
Find the most recent MSc Applied Computational Science and Engineering Programme Specification on the Imperial College London course page for the MSc Applied Computational Science and Engineering.
Who is the MSc for?
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++).
Why should I apply for the MSc?
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”
- Model dynamical processes using numerical methods and advanced programming
- Large scale, big data, machine learning
- Combining mathematics, physical sciences, engineering, and computational science
- Preparing tomorrow’s technologists, entrepreneurs and computational problem solvers
Two MSc + PhD departmental scholarships for women – apply by 30 May 2022
ESE is pleased to offer two departmental scholarships available to women for an MSc plus PhD, for 2022 entry.
The scholarships will be awarded to women who demonstrate exceptional academic merit and/or potential and are open to Home and Overseas applicants.
The Scholarship will cover tuition fees for Home or Overseas students and a bursary for the one-year MSc course, followed by a fully-funded 3.5 year PhD in the Department of Earth Science and Engineering. To be eligible for a scholarship, candidates must fulfil the course’s eligibility criteria and must have submitted an application for the MSc course through the Imperial College MSc programme admissions process including submitting all required documents.
To be considered for a Scholarship for one of the above MSc programmes in academic year 2022-23, please:
- Apply for the course via the Imperial College MSc graduate course admissions process by 30 May 2022;
- Request the online application form from the appropriate course administrator (listed above);
- Complete this application form by 11am (UK time) on 13 June 2022.
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
You can see the teaching schedule represented visually below. If you would like an accessible version of this information, please contact ESE webmaster.
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, Dr Saskia Goes, Dr Gerard Gorman (Course Director), Prof Joanna Morgan, Prof Stephen Neethling, Dr Adriana Paluszny, Prof Matthew Piggott, Prof Michael Warner
How to apply and further information
We strive to increase and broaden inclusivity and support everyone, regardless of background, in breaking down any barriers to you applying to the Department. If you are unable to pay a course’s application fee, we encourage you to apply for a fee waiver. If you are interested in an MSc and wish to apply for a fee waiver, please contact firstname.lastname@example.org prior to starting your application.
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