Do you want to solve engineering challenges by using programming and computer modelling? Study Computational Engineering at UiS!
2 years (4 semesters)
English
120
August every year
About the study programme
A master's degree in Computational Engineering will give you knowledge and expertise in engineering, programming and computer modelling. The skillset of Computational Engineering is at the intersection between engineering and data science and will make you well equipped to contribute to the digitization and AI implementation that is happening across the industry and the globe.
If you have a bachelor's degree in engineering (or equivalent) and want skills in how you can combine modern data-driven or machine learning techniques, mathematical modelling and programming to solve engineering challenges, then this is the programme for you.
- You will learn how to use open source programming languages such as Python and its vast library of modules to develop mathematical and machine learning models to automize workflows, and to learn how to apply mathematical and numerical models to analyse complex and uncertain systems. These insights are used to improve decision-making in a range of sectors from health technology to energy and energy transition.
- In the programme, you will meet students from different engineering backgrounds.
- We have four compulsory courses where the focus is on modelling, programming, machine learning, and decision support.
- In our courses we use project work, and you will have opportunities 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 you can choose courses that best suit your interests and/or engineering background.
The study programme is international; Norwegian and international students study together.
Career prospects
The use of digital technology is rapidly increasing and can be seen everywhere. Computational Engineers are trained to contribute in developing a society where the usage and integration of data with machine learning is becoming a must. The ability to combine engineering aspects (domain knowledge) and computational skills is an absolute necessity toward digitalisation and AI implementation.
Modelling and programming skills are highly desirable in any advanced 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.
Learning outcomes
All study programmes at the UiS have a set of defined learning targets. Read more about the learning outcome for this study programme.
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.
Study plan and courses
Enrolment year: 2025
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Compulsory courses
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Decision Analysis with Artificial Intelligence Support
Year 1, semester 1
Decision Analysis with Artificial Intelligence Support (MOD500)
Study points: 10
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Modeling and Computational Engineering
Year 1, semester 1
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Fundaments of Machine Learning for and with Engineering Applications
Year 1, semester 1
Fundaments of Machine Learning for and with Engineering Applications (MOD550)
Study points: 10
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Data-driven Modeling of Conservation Laws
Year 1, semester 2
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Elective courses 2nd semester
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Machine Learning
Year 1, semester 2
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Differential Equations
Year 1, semester 2
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Introduction to Computational Fluid Dynamics
Year 1, semester 2
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Applied Computational Fluid Dynamics for Industrial Processes
Year 1, semester 2
Applied Computational Fluid Dynamics for Industrial Processes (MSK600)
Study points: 5
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Other elective course 2nd semester
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Reservoir Modelling and simulation
Year 1, semester 2
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Select a master's thesis (30 or 60 ECTS) starting 2nd or 3rd semester
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Master's Thesis in Computational Engineering
Year 1, semester 2
Master's Thesis in Computational Engineering (MODMAS60)
Study points: 60
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Master's Thesis in Computational Engineering
Year 2, semester 3
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3rd semester at UiS or Exchange Studies
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Courses at UiS 3rd semester
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Elective courses 3rd semester
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Discrete Simulation and Performance Analysis
Year 2, semester 3
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Image Processing and Computer Vision
Year 2, semester 3
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Developing Research and Presentation Skills
Year 2, semester 3
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Statistical Learning
Year 2, semester 3
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Other elective courses 3rd semester
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Introduction to Data Science
Year 2, semester 3
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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
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Finite Element Methods, Advanced Course
Year 2, semester 3
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Statistical Modeling and Simulation
Year 2, semester 3
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Exchange 3rd semester
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Exchange Studies 3rd semester
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Academic requirements
A bachelor's degree in engineering or equivalent is required. The degree must include at least 10 ECTS credits in computer sciences or computer engineering courses, or an introductory course for engineers including programming. Applicants must have the equivalent of 25 ECTS credits in mathematics, 5 ECTS credits in statistics and 7,5 ECTS credits in Physics.
Admission to this master's programme requires a minimum grade average comparable to a Norwegian C (according to ECTS Standards) in your bachelor's degree. Applicants with a result Second-class lower Division or lower are not qualified for admission.
Application and admission
Contact
For inquiries regarding admission to international Master's programmes, send an email to: admissions@uis.no
Student life at UiS
Student exchange
By going on exchange to one of our partner institutions abroad as part of your studies, you will have an opportunity to get a unique education. In addition to improving your career opportunities, you grow as a person and gain the ability to greater reflect on the topics you study as part of your degree.
Schedule for the exchange
Students are encouraged to 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, or in a first stage of the master’s thesis. During the exchange semester, you can choose courses relevant to the master’s programme, and also depending on personal interests and career opportunities. The courses or the thesis project you want to take abroad must be approved by the department. It is important that the courses from abroad do not overlap with courses you have already taken. An advice is to think about your professional career and your fields of specific interest.
As a reminder, you shall choose at least one non-science/technological course equivalent to 5-10 ECTS (e.g. economics, languages, ethics, project management, green transition or similar).
More opportunities
In addition to the recommended universities listed below, UiS has a number of agreements with universities outside of Europe that are applicable to all students at UiS, provided that they find a relevant course offering. Within the Nordic region, all students can use the Nordlys and Nordtek networks.
Svalbard
Students may choose to take courses at UNIS in Svalbard. More information here.
Contact your study adviser at the faculty if you have questions about guidance and pre-approval of courses.
General questions about exchange:
Go to the exchange guide in the Digital student service desk
See where you can travel
Frequently asked questions
Is it possible to take the study as a distance student?
Some of the courses included in the study programme may be taken digitally as a distance student. However, several of the courses on the programme have compulsory laboratory activities, which requires that one is present. You can always consult with the study adviser or lecturers if you are wondering if a specific course can be completed digitally.
Can I take this study part time?
We do not have a separate part-time plan for this study. But you get the right to study for 3 years by admission to the master's programme, with the possibility of a fourth year to complete. This means that you can set up an individual education plan that is not a full-time study. It is a good idea to contact the study adviser so that you can make a plan together.
When and where can I travel on exchange?
3rd semester, i.e. the second year of the study is arranged for exchange.
Can I take courses in Svalbard as part of the degree?
Yes, we recommend that you take courses at the University Center on Svalbard (UNIS) as part of your degree in Computational Engineering. UNIS has available courses in both the spring semester, the autumn semester, and summer courses. Feel free to contact UNIS contact Karina Sanni: karina.sanni@uis.no or check UNIS's website for more information.
Can I apply for single courses at UiS?
Contact
Faculty Administration TN
Kontor for utdanningsadministrative tjenester
Department of Energy Resources