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Computational Engineering, Master of Science Degree Programme


Study programme description for study year 2022-2023

Facts
Credits (ECTS)

120

Studyprogram code

M-COMPEN

Level

Master's degree (2 years)

Leads to degree

Master of Science

Duration

4 Semesters

Undergraduate

No

Language of instruction

English

A master’s degree in computational engineering makes you eligible for the most in demand and interesting tasks in the private or public sector as an engineer, researcher or leader. 
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. 

Computational engineers focus on the development and application of mathematical and numerical models to analyse complex and uncertain systems for gaining knowledge and insights into the systems and using these knowledge and insights to support decision making. The main emphasis in computational engineering is on modelling. Data is an important source of understanding systems and can be used to refine models. Thus, a key aspect of computational engineering is to bridge scientific theories and data science in applications. 

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 modelling skills. Many companies, including all major energy and service companies, research institutes and many of their spin-off companies seek this competence. 
 
The program is international and includes Norwegian and foreign students. All courses are taught in English.  

 
The program includes advanced topics in modelling and algorithms, decision analysis, optimization, and uncertainty quantification. Master in Computational Engineering is a post-graduate program that runs over four semesters and covers 120 ECTS, resulting in a master’s degree in computational engineering 

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: 

Advanced 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

Syllabus

Programme content, structure and composition  

After the student is admitted to the 2-years master programme in Computational Engineering, the student must take a test in programming and system administration. If the test is not passed, the University of Stavanger offers and advices the student to take a preparatory summer course in programming and system administration. The course is being taught early in August and before the official semester starts. The purpose of the summer course is to prepare the student for the master’s programme in the best possible way. The University of Stavanger does not consider necessary to offer the preparatory summer course to students who have passed the following courses at the University of Stavanger:  
- 10 ECTS in programming and a minimum of 5 ECTS in operating systems.  
 
The master programme in Computational Engineering is a two-year full time study consisting of 120 ECTS. 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 specialization courses and a Master’s thesis of 30 ECTS. The Master 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. You will get training in writing reports and communicate your 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 third semester. 

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 

Course assessment

Schemes for quality assurance and evaluation of studies are stipulated in the Quality system for education

Study plan and courses
  • Compulsory courses

    • Master's Thesis in Computational Engineering

      Year 2, semester 3

      Master's Thesis in Computational Engineering

      Study points: 30

  • 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  

          Study points: 10

        • Developing Research and Presentation Skills

          Year 2, semester 3

          Developing Research and Presentation Skills

          Study points: 10

        • Economics and Decision Analysis for Engineers

          Year 2, semester 3

          Economics and Decision Analysis for Engineers

          Study points: 10

        • Statistical learning

          Year 2, semester 3

          Statistical learning

          Study points: 10

      • Other elective courses 3rd semester

        • Discrete Simulation and Performance Analysis

          Year 2, semester 3

          Discrete Simulation and Performance Analysis

          Study points: 10

        • Introduction to data science

          Year 2, semester 3

          Introduction to data science

          Study points: 10

        • Finite Element Methods, Advanced Course

          Year 2, semester 3

          Finite Element Methods, Advanced Course

          Study points: 10

        • Statistical modeling and simulation

          Year 2, semester 3

          Statistical modeling and simulation

          Study points: 10

    • Exchange 3rd semester

  • 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

  • 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

    • Modeling and Control for Automation Processes

      Year 1, semester 2

      Modeling and Control for Automation Processes

      Study points: 10

  • Other elective courses 1st and 2nd semester

  • 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  

          Study points: 10

        • Developing Research and Presentation Skills

          Year 2, semester 3

          Developing Research and Presentation Skills

          Study points: 10

        • Economics and Decision Analysis for Engineers

          Year 2, semester 3

          Economics and Decision Analysis for Engineers

          Study points: 10

        • Statistical learning

          Year 2, semester 3

          Statistical learning

          Study points: 10

      • Other elective courses 3rd semester

        • Introduction to data science

          Year 2, semester 3

          Introduction to data science

          Study points: 10

        • Integrated reservoir management: From data to decisions

          Year 2, semester 3

          Integrated reservoir management: From data to decisions

          Study points: 10

        • Finite Element Methods, Advanced Course

          Year 2, semester 3

          Finite Element Methods, Advanced Course

          Study points: 10

        • Statistical modeling and simulation

          Year 2, semester 3

          Statistical modeling and simulation

          Study points: 10

    • Exchange 3rd semester

Student exchange

Study abroad semester
3rd semester

Applicants admitted to the program are recommended to go on an exchange semester abroad as a part of their studies. UiS regards this exchange as a highly positive component of the program which will increase your employability. Students should select from their study program’s recommended universities. 

Schedule for the exchange

Recommended semester abroad is 3rd semester. This semester has 30 ECTS elective courses. When studying abroad you must select courses that provide an equivalent specialization in the subject area. These courses should not overlap with the courses you have already completed. We recommend that you also consider your field of interest. 

More opportunities

In addition to the recommended universities listed below, UiS has several 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. More information.

Contact person: 
Guidance and pre-approval of courses:
Karina Sanni: karina.sanni@uis.no 

General inquiries regarding exchange available in: Digital studentekspedisjon

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.

Admission 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. 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.

Contact information

Study Adviser:   Karina Sanni

Study Programme Coordinator: Aksel Hiorth