Master of Science in Computational Engineering

A master's degree in Computational Engineering will give you knowledge and expertise in engineering, programming and computer modeling. The skillset of Computational Engineering is at the intersection between engineering and data and will make you well equipped to contribute to the digitization that is happening across the industry.

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About the programme
Duration

2 years (4 semesters)

Language of instruction

English

ECTS credits

120

Start of studies

August every year

If you have a bachelor's degree in engineering (or equivalent) and want skills in how you can combine modern data-driven techniques, mathematical modeling and programming to solve engineering challenges, then this is the programme for you. In this study, you will learn how to apply mathematical and numerical models to analyze complex and uncertain systems. The insights are used to make better decisions about improved performance, quality, and workflows.

In the course, you will meet students from different engineering backgrounds. We have three compulsory subjects where the focus is on modeling, programming and decision support. In our subjects 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 subjects that best suit your interests and/or engineering background.

The study programme is international; Norwegian and international students study together. All subjects 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 program in engineering or science.

The programme includes advanced topics in:

  • modeling and algorithms,
  • decision analysis, and
  • optimization and uncertainty quantification.

There is a great demand for this competence in society. Several of our students receive relevant job offers before they have completed their master's degree. Some work with data analysis, some develop and test programs, while others work as engineers. The study runs over four semesters and covers 120 ECTS credits and results in a master's degree in Computational Engineering.

Video: NORCE

Career prospects

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.  
 
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 digitalization steps. 
 

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 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 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.

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.

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. Applicants with a result Second-class lower Division or lower are not qualified for admission.

Supplementary rules for admission

Application and admission

Deadlines

and other admission requirements

Interview with former students

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 (MOD500)

      Study points: 10

    • Modeling and Computational Engineering

      Year 1, semester 1

      Modeling and Computational Engineering (MOD510)

      Study points: 10

    • Mathematical and Numerical Modelling of Conservation Laws

      Year 1, semester 2

      Mathematical and Numerical Modelling of Conservation Laws (MOD600)

      Study points: 10

    • Master's Thesis in Computational Engineering

      Year 2, semester 3

      Master's Thesis in Computational Engineering (MODMAS)

      Study points: 30

  • Elective courses

    • Introduction to Data Science

      Year 1, semester 1

      Introduction to Data Science (DAT540)

      Study points: 10

    • Economics and Decision Analysis for Engineers

      Year 1, semester 1

      Economics and Decision Analysis for Engineers (PET685)

      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 (MOD550)

      Study points: 10

    • Computational Fluid Dynamics (CFD)

      Year 1, semester 2

      Computational Fluid Dynamics (CFD) (MSK610)

      Study points: 10

    • 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

    • Mathematical and Numerical Modeling of Battery

      Year 1, semester 1

      Mathematical and Numerical Modeling of Battery (ENE210)

      Study points: 5

    • Computational Reservoir and Well Modeling

      Year 1, semester 1

      Computational Reservoir and Well Modeling (PET510)

      Study points: 10

    • Probability and Statistics 2

      Year 1, semester 1

      Probability and Statistics 2 (STA500)

      Study points: 10

    • Discrete Simulation and Performance Analysis

      Year 1, semester 2

      Discrete Simulation and Performance Analysis (DAT530)

      Study points: 10

    • Machine Learning

      Year 1, semester 2

      Machine Learning (ELE520)

      Study points: 10

    • Differential Equations

      Year 1, semester 2

      Differential Equations (MAT320)

      Study points: 10

  • 3rd semester at UiS or Exchange Studies

    • Courses at UiS 3rd semester

      • Elective courses 3rd semester

        • Reservoir Modelling and simulation

          Year 2, semester 3

          Reservoir Modelling and simulation (GEO506)

          Study points: 10

        • Developing Research and Presentation Skills

          Year 2, semester 3

          Developing Research and Presentation Skills (GEO620)

          Study points: 10

        • Economics and Decision Analysis for Engineers

          Year 2, semester 3

          Economics and Decision Analysis for Engineers (PET685)

          Study points: 10

        • Statistical Learning

          Year 2, semester 3

          Statistical Learning (STA530)

          Study points: 10

      • Other elective courses 3rd semester

        • Introduction to Data Science

          Year 2, semester 3

          Introduction to Data Science (DAT540)

          Study points: 10

        • Reservoir Modelling and simulation

          Year 2, semester 3

          Reservoir Modelling and simulation (GEO506)

          Study points: 10

        • Integrated Reservoir Management: From data to decisions

          Year 2, semester 3

          Integrated Reservoir Management: From data to decisions (GEO608)

          Study points: 10

        • 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

        • Finite Element Methods, Advanced Course

          Year 2, semester 3

          Finite Element Methods, Advanced Course (MSK540)

          Study points: 10

        • Statistical Modeling and Simulation

          Year 2, semester 3

          Statistical Modeling and Simulation (STA510)

          Study points: 10

    • Exchange 3rd semester

Frequently asked questions

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/

Can I take courses in Svalbard as part of the degree?
Yes, as a student with us, 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 the summer course. Feel free to contact UNIS contact Karina Sanni: karina.sanni@uis.no or check UNIS's website for more information.

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

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

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.

    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.

    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.

    Uppsala universitet

    “In Uppsala you walk in the gardens of Linnaeus, follow in the footsteps of Nobel laureates, and at the same time meet today’s and tomorrow’s smartest teachers and researchers.”

  • 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.

  • 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.

  • 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.

  • 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.

  • Sweden

    Uppsala universitet

    “In Uppsala you walk in the gardens of Linnaeus, follow in the footsteps of Nobel laureates, and at the same time meet today’s and tomorrow’s smartest teachers and researchers.”

  • United States

    Colorado School of Mines

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

Contact

Rådgiver
51831145
Faculty of Science and Technology
Faculty Administration TN
Kontor for utdanningsadministrative tjenester
Professor
51831757
Faculty of Science and Technology
Department of Energy Resources
Our department

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.
We also have a student union; Synergy. Read more about the union here.

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