Computational Engineering - Master of Science Degree Programme

In this programme the students learn how to apply mathematical and numerical models to analyse complex and uncertain systems. The insights are used to make better decisions about improved performance, quality, and workflows.

The programme has students from different engineering disciplines. We have four compulsory courses where the focus is on modeling, programming, machine learning and decision support. In our courses we use project work, and students have the opportunity to work with realistic problems and learn how to present and communicate the results professionally. The rest of the study programme consists of recommended electives, where the student can choose courses that best suit their interests and/or engineering background.

The study programme is international; Norwegian and international students study together. All courses are taught in English. The master's programme introduces, illustrates, and discusses a methodology that is based on mathematics, statistics and basic programming from a bachelor's programme in engineering or science.

The programme includes advanced topics in:

• modeling and algorithms,
• decision analysis, and
• optimization and uncertainty quantification.

Master in Computational Engineering is a post-graduate programme that runs over four semesters and covers 120 ECTS, resulting in a master’s degree in Computational Engineering. 30 ECTS come from courses that ensure a broad and common basis in modelling, programming and decision making. The remaining 90 ECTS consist of 60 ECTS from specialisation courses and a Master’s thesis of 30 ECTS. The Master's thesis is a large, independent project completed in the final semester, often in close cooperation with an external company. All teaching is in English. The courses have weekly lectures, many courses use mandatory hand-in projects as an active learning strategy and as part of a folder evaluation.

The students get training in writing reports and communicate results to a broader audience. Programming and analysing data is an integral part of most courses.

A description of each individual course is provided, detailing:

- Working and teaching methods
- Course literature
- Evaluation methods
- Assessment methods
- Learning outcomes

The master’s thesis (MODMAS) is usually completed in the 4th semester and addresses topics relevant to the study programme. Many students write their thesis with a company or public institution. Planning of the master’s thesis should start in the 3rd semester.

The programme focus on dynamic decision-making under uncertainty, specifically tailored to the energy sector, and offer courses that integrate carbon management strategies with traditional financial metrics, teaching students to evaluate projects that achieve both sustainability and profitability goals. Without unbiased forecasting and computational engineering, energy companies risk poor decisions, hindering progress towards clean energy.

The programme equips the students with the tools and methodologies to navigate this transition effectively. In this way, the Msc in Computational Engineering contributes in reaching the United Nation's Sustainable Development Goal no. 7: Affordable and Clean Energy

Learning outcomes

After having completed the master’s programme in Computational Engineering, the student shall have acquired the following learning outcomes, in terms of knowledge, skills and general competences:

Knowledge

K1: Can demonstrate the competence in the field of uncertainty quantification and advanced modelling for decision support. This means that the candidate has the ability to develop mathematical models that account for uncertainties contained in incomplete data and information and provide the basis for improved understanding and interpretation of data as well as for decision support.

K2: Has knowledge of a range of mathematical and data science models to be able to determine suitable mathematical formulation to describe a system.

K3: Has knowledge of numerical solution methods to be able to quantify limitations in the mathematical models and the numerical errors introduced by the solution methods.

Skills

S1: Is able to analyse and act critically to different sources of information and apply them to structure and formulate professional and scientific reasoning according to modelling, uncertainty quantification, simulation, optimization and decision support.

S2: Has detailed knowledge and experience of programming in at least one high level programming language. Develop custom modelling programs for specific decision- or optimization situations.

S3: Can collect, analyse and critically evaluate suitable datasets to test models. Tune model parameters using data and expert knowledge. Perform sensitivity analysis of model parameters to generate additional insights and understanding.

S4: Is able to find the right balance between a model's usefulness (how credible is the understanding generated by the model) and manageability (any analysis must be completed within given time and resource constraints).

S5: Can carry out an independent, limited research or development project under supervision and in accordance with applicable norms for research ethics.

General Competence

G1: Is able to develop hypotheses and suggest systematic ways to test these using mathematical models.

G2: Can communicate in a professional way about scientific problems, decisions, results of data, uncertainty, and modelling analysis -both to specialists and to the general public.

G3: Is able to use mathematical modelling as a tool in a wide range of problems and applications in varying disciplines and contribute to innovation.

G4: Can analyse relevant academic, professional and research ethical problems.

Career prospects

The use of digital technology is rapidly increasing and can be seen everywhere. Computational Engineers are crucial in developing a society where the usage and integration of data is a significant activity, because they have specific knowledge of the engineering aspects (domain knowledge) and computational skills to take the necessary digitalisation steps.

Modelling skills and programming are necessary in almost every industry.
Some examples of industries and businesses where students can find employment are: Energy, consulting and service companies, hospitals and other public agencies.

A Master’s degree in Computational Engineering gives a solid foundation for admission to PhD studies in the areas relevant to the chosen academic specialisation. In particular, the PhD studies in Energy and Petroleum Technology as well as in Information Technology, Mathematics and Physics.

Course assessment

Schemes for quality assurance and evaluation of studies are stipulated in the Quality system for education