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Master of Science in Computational Engineering

MSc in Computational Engineering makes you eligible for the most demanding and interesting tasks in the private or public sector as an engineer, researcher, or leader. Computational engineers are needed in diverse industries where the domain knowledge of engineering and computational modeling are required.

Publisert: Endret:
Facts
Duration

2 years/4 semesters

Number of study places

10 for local applicants 10 for international applicants

ECTS credits

120

Language of instruction

English

Study start

August every year

No tuition fees

Angela explains why she chose to study Computational Engineering.

Meet a student

The future of petroleum engineers is just that; a combination of data, models and gathering information to make better decisions.

Vibeke Haugen , R&T manager Equinor

Computer engineers focus on the design and development of computer systems and other technological devices; for example, designing operating systems and chips.

Data scientists focus on the discovery and interpretation of hidden patterns in data for extracting knowledge and insights from data.

Computational engineers focus on the development and application of mathematical and numerical models to analysis complex and uncertain systems for gaining knowledge and insights into the systems.

The ultimately goal of both data science and computational engineering is to support decision making, but the main emphasis in data science is data whilst the main emphasis in computational engineering is models (including data-driven models).

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. 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 modeling skills. Many companies, including all major oil, service companies, research institutes and many of their spin-off companies seek this competence.

The programme is international, where Norwegian and foreign students study jointly. All of the courses are taught in English. The Master programme introduces, illustrates, and discusses a methodology that builds on mathematics, statistics and basic programming from a Bachelor program in engineering or science.

The programme includes advanced topics in modeling and algorithms, decision analysis, optimization, and uncertainty quantification. The study programme is a post-graduate programme that runs over four semesters and covers 120 ECTS, resulting in a Master degree in Computational Engineering.

Information in Norwegian.

Decisions and Modeling

Good decisions rely on domain knowledge, relevant data, good models, and the ability to model.

By acquiring skills within modeling and understanding how a model can be used for decision-making support, you will be better equipped to create value from digitalization. Real world problems change all the time. The technical depth and the programming skills provided by this master’s program will give students the required technical competence and flexibility to handle these challenges. The study program will give you skills to analyze complex unstructured problems and apply the insight to make better decisions to improve e.g. efficiency, quality, and workflows.

Courses

The master’s program has courses that build on mathematics, statistics, and basic programming from a bachelor’s program in engineering or science. The study program contains advanced modeling and algorithmic topics, decision analysis, optimization, and uncertainty modeling.

A master's degree in Computational Engineering makes you eligible for the most demanding and interesting tasks in the private or public sector as an engineer, researcher, or leader. Job opportunities are abundant, and the majority of major oil and service companies and research institutes (SINTEF, NORCE, IFE) demand this competence.

University of Stavanger offers and encourages the student to take a preparatory summer course in programing and system administration. The course is being taught early in August and before the official semester starts. 

Video: NORCE

Career prospects

Modeling skills and abilities are necessary in almost every industry. Some examples of industries and businesses where students can find employment are: Oil and energy, consulting and service companies, hospitals and other public agencies. The use of digital technology is rapidly increasing and can be seen everywhere. Computational Engineers are absolutely crucial in realizing the information society, because they have specific knowledge of the engineering aspects (domain knowledge) and computational skills to take the necessary digitalization steps. 

A Master 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 energy and petroleum technology as well as in information technology, mathematics and physics are relevant

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 competence:

Knowledge

K1: Has advanced knowledge in the field of uncertainty quantification and modeling 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: Advanced knowledge of effective methods for designing, developing and testing models.

K3: Advanced knowledge in the use of algorithms and computational thinking to solve discrete and continuous problems.

K4: Understand the limitations introduced by representing a complex system with a model.

K5: Understand the constraints associated with the chosen solution method, including approximation errors and constraints linked to the selection of specific algorithms or numerical methods.

K6: Understand the importance of quantifying relevant and material uncertainties to generate insight and informed decisions.

K7: Deep understanding of the significance and consequences imbedded in the well-known quote: “All models are wrong, but some models are useful” (George Box, 1978).

Skills

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

S2: Detailed knowledge and experience of programming in at least one high level programming language.

S3: Determine model parameters using data and expert knowledge.

S4: Be 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: Develop custom modeling programmes for specific decision- or optimization situations.

S6: Model systems and develop new instruments and applications for gathering relevant data, analysis and management in accordance with established engineering principles.

S7: Evaluate instruments and applications to quantify the value of information and to optimize the data gathering, analysis and management.

S8: Perform sensitivity analysis of model parameters to generate additional insights and understanding.

General Competence

G1: Develop hypotheses and suggest systematic ways to test these using mathematical models.

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

G3: Utilize the generic nature that lies in the use of mathematical formulations to actively seek to transfer knowledge between different applications.

G4: Utilize the mathematical formulation to gain insight into the core of the problem that is uncover the most basic mechanisms that govern the process being studied.

G5: Insight into “The Art and Science of Mathematical Modeling”.

Academic requirements

A bachelor's degree in engineering or equivalent is required.

The degree must include at least 10 ECTS credits in informatics/computer sciences/an introductory course for engineers including programming

All applicants must have at least 30 ECTS credits in mathematics/statistics/calculus.

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.

Application and admission

How and when to apply?

Deadlines, English requirements, documentation requirements and more

Contact

For inquiries regarding admission to international Master's programmes, send an email to: admissions@uis.no

Study plan and courses
Already a student? Find the full study programme description and study plan here
  • Compulsory courses

    • Modeling for Decision Insight

      YEAR 1 SEMESTER 1

      Modeling for Decision Insight

      Study points: 10

    • Modeling and Computational Engineering

      YEAR 1 SEMESTER 1

      Modeling and Computational Engineering

      Study points: 10

    • Mathematical and Numerical Modelling of Conservation Laws

      YEAR 1 SEMESTER 2

      Mathematical and Numerical Modelling of Conservation Laws

      Study points: 10

    • Master's Thesis in Computational Engineering

      YEAR 2 SEMESTER 3

      Master's Thesis in Computational Engineering

      Study points: 30

  • Choose one course in 2nd semester

    • Applied Data Analytics and Statistics for Spatial and Temporal Modeling

      YEAR 1 SEMESTER 2

      Applied Data Analytics and Statistics for Spatial and Temporal Modeling

      Study points: 10

    • Modeling and control for automation processes

      YEAR 1 SEMESTER 2

      Modeling and control for automation processes

      Study points: 10

  • Elective courses

    • Introduction to data science

      YEAR 1 SEMESTER 1

      Introduction to data science

      Study points: 10

    • Economics and Decision Analysis for Engineers

      YEAR 1 SEMESTER 1

      Economics and Decision Analysis for Engineers

      Study points: 10

    • Applied Data Analytics and Statistics for Spatial and Temporal Modeling

      YEAR 1 SEMESTER 2

      Applied Data Analytics and Statistics for Spatial and Temporal Modeling

      Study points: 10

    • Computational Fluid Dynamics (CFD)

      YEAR 1 SEMESTER 2

      Computational Fluid Dynamics (CFD)

      Study points: 10

    • Integrated Reservoir Management From Seismic Field Development Planning

      YEAR 1 SEMESTER 2

      Integrated Reservoir Management From Seismic Field Development Planning

      Study points: 10

  • Other elective courses 1st and 2nd semester

  • 3rd semester at UiS or Exchange Studies

    • Courses at UiS 3rd semester

    • Exchange 3rd semester

Frequently asked questions

Can I complete this study without much programming experience?

You may be able to complete the study even if you do not have much programming experience. We see that students who complete the preparatory summer course in programming and systems administration before the first semester, on average, achieve better results in the study than students who do not take this course. This normally takes place over a few days in August. Based on your result on the digital summer exam, we will tell you if we think you should take the preparatory summer course.

There are no credits or grades on the preparatory summer course.

The preparatory digital test I will take when I have been admitted to the study - will it be registered on my diploma?

No, this result is not recorded on either the transcript or diploma. How you do it on this exam has no practical significance, but based on your test score, we will give you a statement on whether we recommend you take the preparatory summer course in programming and systems organization before the first semester.

Is it possible to take the study as a distance student?

Some of the subjects included in the study program may be taken digitally as a distance student. However, several of the subjects on the program have compulsory laboratory activities, which requires that one is present. You can always consult with the study coordinator 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 program, 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 coordinator so that you can make a plan together.

When and where can I travel on exchange?

3rd semester as an exchange semester, ie the second year of the study is arranged for exchange. We have several agreements in and outside Europe, and you can check the updated list of universities you can travel to here:

https://student.uis.no/studieprogram-og-emner/ingenior-og-sivilingenior/toarig-master-i-teknologi-siv-ing/computational-engineering/utvekslingsmuligheter/

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. All about exchange

Exchange semester

Semester 3



Exchange scheme

In semester 3 of the Master program in Computational Engineering, you have the possibility to study abroad at one of UiS partner universities. This semester has 30 ECTS elective courses.



When going abroad, you must choose courses that comprise a similar specialization within your field, and these must be pre-approved before you leave. It is also important that the courses you are going to take abroad do not overlap in content with the courses you have taken or will take later in the study program. A good tip is to think about your specialization and your field of interest.



It is recommended to start planning the exchange early.



More possibilities

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.  



Contact person

Guidance and pre-approval of courses:

Karina Sanni



General information about exchange: Exchange guide in Digital student service desk

+
Student exchange
  • All countries

    Aalborg Universitet

    Aalborg Universitet (AAU) er kjent for å benytte seg av problembasert læring i grupper, noe som kan by på en spennende læringsprosess.

    Politecnico di Milano University

    Politecnico di Milano er Italias største tekniske universitet med om lag 40.000 studenter og er høyt rangert på en rekke internasjonale rankinglister.

    Technical University of Munich

    The Technical University of Munich, also known as TUM, accounts for major advancements in the field of natural sciences. TUM is one of the best universities in Germany and has several awarded scientists and Nobel Prize winners. The Technical University of Munich strives for excellent teaching and research quality.

    Colorado School of Mines

    Colorado School of Mines (CSM) er et offentlig universitet kjent verden over for sin gode ingeniørutdannelse.

    Griffith University

    Griffith University er en populær utvekslingsdestinasjon for UiS-studenter. Universitetet er et særlig godt valg for studenter innen musikk/dans, hotell/turisme og business.

  • Denmark

    Aalborg Universitet

    Aalborg Universitet (AAU) er kjent for å benytte seg av problembasert læring i grupper, noe som kan by på en spennende læringsprosess.

  • Italy

    Politecnico di Milano University

    Politecnico di Milano er Italias største tekniske universitet med om lag 40.000 studenter og er høyt rangert på en rekke internasjonale rankinglister.

  • Germany

    Technical University of Munich

    The Technical University of Munich, also known as TUM, accounts for major advancements in the field of natural sciences. TUM is one of the best universities in Germany and has several awarded scientists and Nobel Prize winners. The Technical University of Munich strives for excellent teaching and research quality.

  • United States

    Colorado School of Mines

    Colorado School of Mines (CSM) er et offentlig universitet kjent verden over for sin gode ingeniørutdannelse.

  • Australia

    Griffith University

    Griffith University er en populær utvekslingsdestinasjon for UiS-studenter. Universitetet er et særlig godt valg for studenter innen musikk/dans, hotell/turisme og business.

Contact

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Department of Energy Resources

The Department of Energy Resources is a strong academic environment with international top-level research and education.

The department boasts an international environment, with scientific staff conducting research into energy resources, technology for improved oil recovery (IOR), decision analysis and geology.

The study programmes at the department cover search and exploration of petroleum and natural resources. Internationalization is a priority, with the development of study programs taught in English and high mobility among scientific staff and students.

The department contributes significantly to research activities and management of the The National IOR Centre of Norway, established by the Norwegian Ministry of Petroleum and Energy in 2013.

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