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Predictive Modelling and Scientific Computing (MSc/PGDip/PGCert/PGA)

Find out more about our Predictive Modelling and Scientific Computing taught Master’s degree.

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P-H1B1 (MSc)

P-H1B5 (PGDip)

P-H1B6 (PGCert)

P-H1B7 (PGA)

MSc/PGDip/

PGCert/PGA

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

Part time: 2 years (MSc), 18 months (PGDip, PGCert), 1 year (PGA)

29 September 2026

Engineering

University of Warwick

Train in the theory and practical implementation of cutting-edge predictive modelling techniques.

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 builds on our world-leading research at the Warwick Centre for Predictive Modelling. The course educates future specialists in computational science and engineering, building on students’ existing programming skills and 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.

This course will equip graduates for further study in areas of critical science and technological significance, 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.

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.

Contact Hours

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. 

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

You will experience a variety of assessment methods, and can expect to mostly sit examinations and complete coursework assignments.

Reading lists

If you would like to view reading lists for current or previous cohorts of students, most departments have reading lists available through Warwick Library on the Talis Aspire platform.

You can search for reading lists by module title, code or convenor. Please see the modules tab of this page or the module catalogue.

Please note that some reading lists may have restricted access or be unavailable at certain times of year due to not yet being published. If you cannot access the reading list for a particular module, please check again later or contact the module’s host department.

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.

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 covering these skills is provided at https://warwick.ac.uk/fac/sci/wcpm/pmsc/mathsinduction.

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.

• 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.

This course is part of the University’s flagship ‘Science for Sustainable Futures’ portfolio – an interdisciplinary initiative designed to address global challenges through innovative education. With core material delivered by leading experts from the School of Engineering and Warwick Mathematics Institute, and optional modules from WMG, Physics, Chemistry, Statistics, Computer Science and research centres (Warwick Centre for Predictive Modelling, HetSys Centre for Doctoral Training, MathsSys Centre for Doctoral Training), the course offers a truly cross-disciplinary experience. By learning across traditional subject boundaries and honing both critical thinking and practical skills, students will gain the advanced knowledge and applied insight sought after by industry leaders and employers across sectors.

Three core modules (ES98A Fundamentals of Predictive Modelling, MA934 Numerical Algorithms and Optimisation, ES98E Scientific Machine Learning) provide students with skills in the three main underlying topics of predictive modelling, scientific computing and scientifically informed machine learning.

Students apply these skills in one (or more) of the three optional core modules (MA9M4 Modelling and Computation of Fluid Dynamics Across Phases and Scales, covering fluid dynamics, ES98D Particle-based modelling, and ES98F Predictive Modelling of Advanced Engineering Materials, covering solid mechanics). Further optional modules are selected from across the departments according to their interest.

All students are trained in relevant transferable skills in the ES98B Predictive Modelling Group Project, covering topics such as collaborative writing, programming and presentation skills. Students also complete an individual research project supervised by one of the academics in the Faculty.

Optional modules

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We have revised the information on this page since publication. See the edits we have made and content history.