Explore our Predictive Modelling and Scientific Computing taught Master’s degree.

• H1B1 (MSc);
• H1B5 (PGDip);
• H1B6 (PGCert);
• H1B7 (PGA)

MSc/PGDip/

PGCert/PGA

Full time: 1 year (MSc), 9 months, (PGDip/PGCert)

Part time: 2 years (MSc), 18 months (PGDip/PGCert), 6 months (PGA)

30 September 2024

Engineering

University of Warwick

Our Predictive Modelling and Scientific Computing MSc trains students in the theory and practical implementation of cutting-edge predictive modelling techniques, exposing them to established, as well as emerging, applications in science and engineering.

Predictive Modelling is a fusion of mathematical modelling, machine learning and scientific computing, providing a powerful new way of thinking about how to model complex systems and improve technology and design.

Enhancements in computer processing power and access to ‘Big Data’ have led to a growth in the number of applications of predictive modelling into areas as diverse as environmental science, energy, healthcare, materials engineering, food science and geology. Our MSc in Predictive Modelling and Scientific Computing educates future specialists in computational science and engineering, equipping them to apply appropriate computational techniques to understand, define and develop solutions to a range of science and engineering problems, including those of national and global importance.

Core modules cover uncertainty quantification and predictive modelling, scientific computing and scientific machine learning, whilst the choice of three optional modules gives students the opportunity to specialise the application focus of the course to align with their interests. Students will participate in individual and group research projects, as well as writing reports and presenting technical work, thus developing the project management and numerical skills sought by employers.

The MSc can be studied part-time over two years to suit those in employment. PG Diploma, Certificate and Award options are also available for those who would like to take a subset of modules.

This course is running for the first time in 2023 and will equip graduates for further study in areas of critical science and technological importance, or for employment in a broad range of data-intensive industries where modelling, design and decision making under uncertainties is important. We have strong links with a range of potential employers.

Core modules cover uncertainty quantification and predictive modelling, scientific computing and scientific machine learning, whilst the choice of three optional modules gives students the opportunity to specialise the application focus of the course to align with their interests. Students will have the opportunity to participate in individual and group research projects, as well as to write reports and present technical work, thus developing the project management and numerical skills sought by employers.

Class sizes for lectures, practical laboratory sessions and seminars vary depending on the number of students taking the module.

The MSc degree (totalling 180 credits) comprises:

• One group project with skills training module (30 credits)
• 6 taught modules (15 credits each)
• A research project (60 credits)

The typical workload for a 15-credit module is as follows:

• 20-30 hours of lectures/seminars
• 10-15 hours of supervised computer lab work
• 50 hours of private/directed study
• 60 hours of assessed work

The research project is valued at 60 credits and students should plan to execute around 600 hours of work towards the completion of the project dissertation.

A combination of coursework and written examinations.

Your timetable

Your personalised timetable will be complete when you are registered for all modules, compulsory and optional, and you have been allocated to your lectures, seminars and other small group classes. Your compulsory modules will be registered for you and you will be able to choose your optional modules when you join us.

A minimum 2:1 undergraduate UK Honours degree or equivalent international qualification, in an engineering, physical sciences or mathematical subject.

You can see how your current degree score or GPA equates to the British system in our Study pages in the Equivalent scores table.

We are willing to consider applications from students with lower qualifications on a case-by-case basis, particularly when the applicant can evidence relevant employment, practical experience or strong performance in undergraduate modules related to their proposed postgraduate course of study.

Please note that applicants will need post A2 Level (or equivalent) knowledge in Mathematics, covering topics such as linear algebra, calculus and analysis, including differential equations, as well as probability and statistics. This could be gained through mathematics modules taken as part of an undergraduate course. It is expected that candidates have a good understanding of these topics at the start of their MSc studies. Self-study resources and a self-assessment test can be found here.

You can find out more about our English language requirements. This course requires the following:

• Band A
• IELTS overall score of 6.5, minimum component scores not below 6.0

Candidates with professional experience should include their CV with their application.

Fundamentals of Predictive Modelling (ES98A) (15 credit)

This module provides students with fundamental knowledge for predictive modelling and uncertainty quantification. It gives an overview of the essential elements of the mathematical, statistical, and computational techniques needed to provide well-calibrated predictions for the behaviour of physical systems.

Numerical Algorithms and Optimisation (MA934) (15 credit)

This module provides students with knowledge (and practice) of important numerical optimisation concepts at the intersection between mathematics and scientific computing. Algorithmic structures, data structures, numerical method construction and performance assessment will form key parts of the module, with applications and use cases concentrated on topics in linear algebra, signal processing and optimisation.

Scientific Machine Learning (ES98E) (15 credit)

This module provides students with knowledge in the modern field of scientific machine learning, which is a fusion of scientific computing and machine learning. Students will learn how to use a variety of statistical and machine learning techniques to train models which combine data-driven and mechanistic models and assess their ability to make useful predictions.

Predictive Modelling Group Project (ES98B) (30 credit)

Predictive modelling group project with training in key professional and research skills and collaborative writing. Groups of students will create a complex piece of predictive modelling research software using methods and design principles introduced in previous modules in the course.

Individual Research Project (ES98C) (60 credit)

Each student will conduct significant and novel research as an individual project, and present the background and findings in the form of a dissertation. The research question must address some aspect of modelling, resulting in new knowledge, methodology or understanding, accompanied by uncertainty quantification.

Optional modules

The selection of optional modules is likely to include:

• Modelling and Computation of Fluid Dynamics Across Phases and Scales (MA9M4)*
• Particle-based Modelling (ES98D)*
• Modelling and Simulation of Engineering Materials (ES98F)*
• Mathematical and Computer Modelling (ED4C3)
• Quantum Chemistry (PX919)
• High Performance Computing (PX457)
• Advanced Topics in Fluids (MA4L0)
• Monte Carlo Methods (ST407)
• Advanced Topics in Data Science (ST419)
• Statistical Learning and Big Data (ST420)
• Continuum Mechanics (MA6J1)
• Advanced Computational Chemistry (CH413)
• Data Mining (CS909)
• Biomolecular Simulation (PX923)

*Please note that students are required to take at least one of these three optional modules.

PG Diploma, Certificate and Award options are also available for those who would like to take a subset of modules. More information

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