Masters Compare - Find your perfect masters course.
Masters Compare - Find your perfect masters course.
Study Details: 12 months full-time
Our MSc Advanced Mathematical Modelling is an advanced course that offers the opportunity to develop skills in understanding, predicting, and solving complex real-world problems.
From climate modelling and fluid dynamics to medical applications and engineering design, the ability to develop and analyse mathematical models is in high demand across industries and research sectors.
This MSc is designed for students who want to apply advanced mathematical techniques to practical challenges. The programme offers:
a strong foundation in applied mathematics, including mathematical biology, fluid dynamics, numerical analysis, and machine learning
hands-on experience in developing and analysing models that describe real-world systems
training in both analytical and computational techniques, ensuring graduates have a versatile skill set
the opportunity to undertake an individual research project, working on cutting-edge problems in collaboration with academic staff and potentially industry partners
Minimum second-class (2:2) honours degree or overseas equivalent* in Mathematics, Computer Science or a closely related discipline containing a strong Mathematical component.
Prospective students with relevant experience or appropriate professional qualifications are also welcome to apply.
See our website for fees
Graduates of the MSc in Advanced Mathematical Modelling will be well-equipped for careers in a wide range of industries, including:
engineering and manufacturing
finance and risk modelling
data science and machine learning
energy and environmental modelling
healthcare and biomedical research
Employers in research, industry, and academia highly value the analytical and computational skills developed throughout the programme. Graduates may pursue roles as mathematical modellers, data analysts, simulation engineers, quantitative researchers, and consultants.
The MSc also provides an excellent foundation for further research at PhD level, particularly in applied mathematics, computational modelling, and interdisciplinary scientific research.
Modelling & Simulation with Applications to Financial Derivatives
Applicable Analysis 3
Fluids & Waves
Finite Element Methods for Boundary Value Problems & Approximation
Optimisation: Theory
Applied Mathematical Methods 1
Foundations of Statistics
Mathematical Biology & Marine Population Modelling
Mathematical Introduction to Networks
Optimisation for Analytics
Medical Statistics
Effective Statistical Consultancy
Survey Design & Analysis
Quantitative Risk Analysis
Bayesian Spatial Statistics
Statistical Machine Learning
Data dashboards with RShiny
Deep Learning
Mathematics of Machine Learning
Numerical Methods & Deep Learning Algorithms for Partial Differential Equation
The place of useful learning The University of Strathclyde is a leading international technological university located in the heart of Glasgow – one ...
Mon 16 Mar 2026