Computational Science: Modelling in Computational Science

This course gives you a broad introduction to key modelling techniques used in computational science. You will learn how to build mathematical models for scientific problems and how to solve them using numerical methods, machine learning and statistical simulations.

You will explore three main modelling approaches

• Differential equation-based modelling: Learn how to solve ordinary differential equations (ODEs) and apply them to problems like cell-reprogramming or disease transmission.
• Monte Carlo methods (MCMC): Understand the Central Limit Theorem, the Metropolis-Hastings algorithm, and how to estimate errors in simulations.
• Machine learning and big data: Use large datasets to train models and explore their use in climate science.

The course also covers common sources of error—such as modelling, discretisation and statistical errors—and discusses how computational science can contribute to sustainable development.

The course is compulsory for the Master’s programmes in Computational Science and Applied Computational Science. It can also be taken as an elective in the Master’s programme in Mathematics and Numerical Analysis, or as a standalone course.  

The course combines lectures with three mandatory group projects. You will work in small teams to solve real problems and present your results both in writing and orally.

Expect a mix of theory and practice. You will write individual project reports and present your work in group seminars. Attendance at all oral presentations is required.

The course is designed to help you develop both technical and collaborative skills. You will learn to plan and carry out tasks within a set timeframe and reflect on the choices you make during the modelling process.  

This course prepares you for advanced studies in computational science, applied mathematics, and datadriven modelling. You will gain skills that are highly relevant in areas such as scientific computing, climate modelling, biostatistics, machine learning, and simulationbased engineering. Whether you continue with a Master’s thesis, pursue doctoral studies, or move into industry, you will acquire practical knowledge of computational tools and methods. The course also encourages critical reflection on how modelling can contribute to sustainable development, highlighting the broader impact of computational approaches in science and society.

Prerequisites

Either a Bachelor's Degree in Physics or at least 90 ECTS credits in natural sciences or engineering, including 43.5 credits in mathematics, of which a course corresponding to NUMA01 Numerical Analysis: Computational Programming with Python, 7.5 credits and 7.5 credits in basic Mathematical statistics. English course 6/B.

Selection criteria

Seats are allocated according to: ECTS (HPAV): 100 %.

Tuition fees for non-EU/EEA citizens

Citizens of countries outside:

• The European Union (EU)
• The European Economic Area (EEA) and
• Switzerland

are required to pay tuition fees. You pay an instalment of the tuition fee in advance of each
semester.

Tuition fees, payments and exemptions

Full programme/course tuition fee: SEK 23,125
First payment: SEK 23,125

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Note that you may also need to pay an application fee, or provide proof of exemption.

Application fee

No tuition fees for citizens of the EU, EEA and Switzerland

There are no tuition fees for citizens of the European Union (EU), the European Economic Area (EEA) and Switzerland.