Computational Engineering - Master of Science Degree Programme

A master’s degree in computational engineering makes you eligible for the most in demand and interesting tasks in the private or public sector as an engineer, researcher or leader.
You will acquire skills that will enable you to analyse complex real world problems, and to use this insight as a foundation for better decisions to, for example, improve performance, quality, and workflows.

Computational engineers focus on the development and application of mathematical and numerical models to analyse complex and uncertain systems for gaining knowledge and insights into the systems and using these knowledge and insights to support decision making. The main emphasis in computational engineering is on modelling. Data is an important source of understanding systems and can be used to refine models. Thus, a key aspect of computational engineering is to bridge scientific theories and data science in applications.

The career opportunities are multiple and, in a world, where digitalization is becoming increasingly important there is a need for candidates with domain knowledge and computational modelling skills. Many companies, including all major energy and service companies, research institutes and many of their spin-off companies seek this competence.

The program is international and includes Norwegian and foreign students. All courses are taught in English.

The program includes advanced topics in modelling and algorithms, decision analysis, optimization, and uncertainty quantification. Master in Computational Engineering is a post-graduate program that runs over four semesters and covers 120 ECTS, resulting in a master’s degree in computational engineering

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: 

Advanced 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

Syllabus

Programme content, structure and composition

After the student is admitted to the 2-years master programme in Computational Engineering, the student must take a test in programming and system administration. If the test is not passed, the University of Stavanger offers and advices the student to take a preparatory summer course in programming and system administration. The course is being taught early in August and before the official semester starts. The purpose of the summer course is to prepare the student for the master’s programme in the best possible way. The University of Stavanger does not consider necessary to offer the preparatory summer course to students who have passed the following courses at the University of Stavanger:
- 10 ECTS in programming and a minimum of 5 ECTS in operating systems.

The master programme in Computational Engineering is a two-year full time study consisting of 120 ECTS. 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 specialization courses and a Master’s thesis of 30 ECTS. The Master 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. You will get training in writing reports and communicate your 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 third semester.

Career prospects

Increased automation, robotization, more use of simulation models and access to large amounts of data changes the traditional engineering work tasks. Computational Engineers are well suited to adopt and contribute to digitalization of the new work tasks, because they have specific knowledge of the engineering aspects (domain knowledge) and computational skills to take the necessary digitalization steps.

Several of our students receive relevant job offers before they have completed the master's degree. Some work with data analyses, some develop and test programs, while others work as engineers.

A Master’s degree in Computational Engineering gives a solid foundation for admission to PhD studies in the areas relevant to the chosen academic specialization. In particular, the PhD studies in , information technology, energy, applied mathematics and physics.

Course assessment

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