Computational Engineering - Master of Science Degree Programme


Study programme description for study year 2024-2025

Facts

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

  • Compulsory courses

    • MODMAS: Master's Thesis in Computational Engineering

      Year 2, semester 3

      Master's Thesis in Computational Engineering (MODMAS)

      Study points: 30

  • 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

          Image Processing and Computer Vision (ELE510)

          Study points: 10

        • 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

          Developing Research and Presentation Skills (GEO620)

          Study points: 10

        • STA530: Statistical Learning

          Year 2, semester 3

          Statistical Learning (STA530)

          Study points: 10

      • Other elective courses 3rd semester

        • DAT530: Discrete Simulation and Performance Analysis

          Year 2, semester 3

          Discrete Simulation and Performance Analysis (DAT530)

          Study points: 10

        • DAT540: Introduction to Data Science

          Year 2, semester 3

          Introduction to Data Science (DAT540)

          Study points: 10

        • 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

          Finite Element Methods, Advanced Course (MSK540)

          Study points: 10

        • 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

          Statistical Modeling and Simulation (STA510)

          Study points: 10

    • Exchange 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

      Modeling and Computational Engineering (MOD510)

      Study points: 10

    • 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

      Data-driven Modeling of Conservation Laws (MOD600)

      Study points: 10

    • MSB415: Sustainable Entrepreneurship

      Year 2, semester 3

      Sustainable Entrepreneurship (MSB415)

      Study points: 10

    • MODMAS: Master's Thesis in Computational Engineering

      Year 2, semester 3

      Master's Thesis in Computational Engineering (MODMAS)

      Study points: 30

  • Elective courses 1st and 2nd semester

    • DAT540: Introduction to Data Science

      Year 1, semester 1

      Introduction to Data Science (DAT540)

      Study points: 10

    • STA500: Probability and Statistics 2

      Year 1, semester 1

      Probability and Statistics 2 (STA500)

      Study points: 10

    • MSK610: Computational Fluid Dynamics (CFD)

      Year 1, semester 2

      Computational Fluid Dynamics (CFD) (MSK610)

      Study points: 10

    • 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

      Computational Reservoir and Well Modeling (PET510)

      Study points: 10

    • 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

      Machine Learning (ELE520)

      Study points: 10

    • GEO506: Reservoir Modelling and simulation

      Year 1, semester 2

      Reservoir Modelling and simulation (GEO506)

      Study points: 10

  • 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

          Image Processing and Computer Vision (ELE510)

          Study points: 10

        • 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

          Developing Research and Presentation Skills (GEO620)

          Study points: 10

        • STA530: Statistical Learning

          Year 2, semester 3

          Statistical Learning (STA530)

          Study points: 10

      • Other elective courses 3rd semester

        • DAT530: Discrete Simulation and Performance Analysis

          Year 2, semester 3

          Discrete Simulation and Performance Analysis (DAT530)

          Study points: 10

        • DAT540: Introduction to Data Science

          Year 2, semester 3

          Introduction to Data Science (DAT540)

          Study points: 10

        • 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

          Finite Element Methods, Advanced Course (MSK540)

          Study points: 10

        • STA510: Statistical Modeling and Simulation

          Year 2, semester 3

          Statistical Modeling and Simulation (STA510)

          Study points: 10

    • Exchange 3rd semester

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 courses relevant to the master programme, 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 courses from abroad not overlap with courses you have already taken. An advice is to think about your professional career and your fields of specific interest. You must 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.

Find out more

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.

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.

  • Danmark

    Aalborg Universitet

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

  • Italia

    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.

  • Sverige

    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.”

  • Tyskland

    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.

  • USA

    Colorado School of Mines

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

Admission 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.

Contact information

Faculty of Science and Technology, tel: +47 51 83 17 00 email: post-tn@uis.no

Study Adviser: Karina Sanni