Master of Science in Computational Engineering

Learning outcomes

After having completed the master’s degree 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.

Student exchange

Schedule for the exchange

Students can go on a study abroad experience during the 3rd semester of the master's programme in Computational Engineering. 

The 3rd semester consists of 30 ECTS credits of flexible courses and electives. During the exchange semester you can choose subjects relevant to the master program, and also depending on personal interests and career opportunities. The courses you want to take abroad must be approved by the department. It is important that the subjects/ courses from abroad not overlap with subjects you have already taken. An advice is to think about your professional career and your fields of specific interest.

More opportunities

In addition to the recommended universities listed below, UiS has a number of agreements with universities outside Europe that are applicable to all students at UiS, provided that they find a relevant subject offering. Within the Nordic region, all students can use the Nordlys and Nordtek networks.

Find out more

Svalbard
Students may choose to take courses at UNIS in Svalbard. More information here. 

Contact your student adviser at the Faculty if you have questions about guidance and pre-approval of courses.

Karina Sanni 

General questions about exchange:

Go to the exchange guide in the Digital student service desk