Computational Engineering - Master of Science Degree Programme
Study programme description for study year 2024-2025
Credits (ECTS)
120
Studyprogram code
M-COMPEN
Level
Master's degree (2 years)
Leads to degree
Master of Science
Full-/Part-time
Full-time
Duration
4 Semesters
Undergraduate
No
Language of instruction
English
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
Study plan and courses
Enrolment year:
-
Compulsory courses
-
MODMAS: Master's Thesis in Computational Engineering
Year 2, semester 3
-
-
3rd semester at UiS or Exchange Studies
-
Courses at UiS 3rd semester
-
Elective courses 3rd semester
-
ELE510: Image Processing and Computer Vision
Year 2, semester 3
-
GEO608: Integrated Reservoir Management: From data to decisions
Year 2, semester 3
Integrated Reservoir Management: From data to decisions (GEO608)
Study points: 10
-
GEO620: Developing Research and Presentation Skills
Year 2, semester 3
-
STA530: Statistical Learning
Year 2, semester 3
-
-
Other elective courses 3rd semester
-
DAT530: Discrete Simulation and Performance Analysis
Year 2, semester 3
-
DAT540: Introduction to Data Science
Year 2, semester 3
-
GEO680: Practical Training in Computational Engineering or Energy, Reservoir and Earth Sciences
Year 2, semester 3
Practical Training in Computational Engineering or Energy, Reservoir and Earth Sciences (GEO680)
Study points: 10
-
MSK540: Finite Element Methods, Advanced Course
Year 2, semester 3
-
PET685: Economics and Decision Analysis for Engineers
Year 2, semester 3
Economics and Decision Analysis for Engineers (PET685)
Study points: 10
-
STA510: Statistical Modeling and Simulation
Year 2, semester 3
-
-
-
Exchange 3rd semester
-
Exchange Studies 3rd semester
-
-
-
Compulsory courses
-
MOD500: Decision Analysis with Artificial Intelligence Support
Year 1, semester 1
Decision Analysis with Artificial Intelligence Support (MOD500)
Study points: 10
-
MOD510: Modeling and Computational Engineering
Year 1, semester 1
-
MOD550: Fundaments of Machine Learning for and with Engineering Applications
Year 1, semester 2
Fundaments of Machine Learning for and with Engineering Applications (MOD550)
Study points: 10
-
MOD600: Data-driven Modeling of Conservation Laws
Year 1, semester 2
-
MSB415: Sustainable Entrepreneurship
Year 2, semester 3
-
MODMAS: Master's Thesis in Computational Engineering
Year 2, semester 3
-
-
Elective courses 1st and 2nd semester
-
DAT540: Introduction to Data Science
Year 1, semester 1
-
STA500: Probability and Statistics 2
Year 1, semester 1
-
MSK610: Computational Fluid Dynamics (CFD)
Year 1, semester 2
-
PET575: Modeling and Control for Automation Processes
Year 1, semester 2
Modeling and Control for Automation Processes (PET575)
Study points: 10
-
-
Other elective courses 1st and 2nd semester
-
PET510: Computational Reservoir and Well Modeling
Year 1, semester 1
-
PET685: Economics and Decision Analysis for Engineers
Year 1, semester 1
Economics and Decision Analysis for Engineers (PET685)
Study points: 10
-
ELE520: Machine Learning
Year 1, semester 2
-
GEO506: Reservoir Modelling and simulation
Year 1, semester 2
-
-
3rd semester at UiS or Exchange Studies
-
Courses at UiS 3rd semester
-
Elective courses 3rd semester
-
ELE510: Image Processing and Computer Vision
Year 2, semester 3
-
GEO608: Integrated Reservoir Management: From data to decisions
Year 2, semester 3
Integrated Reservoir Management: From data to decisions (GEO608)
Study points: 10
-
GEO620: Developing Research and Presentation Skills
Year 2, semester 3
-
STA530: Statistical Learning
Year 2, semester 3
-
-
Other elective courses 3rd semester
-
DAT530: Discrete Simulation and Performance Analysis
Year 2, semester 3
-
DAT540: Introduction to Data Science
Year 2, semester 3
-
GEO680: Practical Training in Computational Engineering or Energy, Reservoir and Earth Sciences
Year 2, semester 3
Practical Training in Computational Engineering or Energy, Reservoir and Earth Sciences (GEO680)
Study points: 10
-
MSK540: Finite Element Methods, Advanced Course
Year 2, semester 3
-
STA510: Statistical Modeling and Simulation
Year 2, semester 3
-
-
-
Exchange 3rd semester
-
Exchange Studies 3rd semester
-
-