Type of course

Can be taken as a single course.

Who has to apply for admission?

• PhD students at UiT that wishes to take a PhD level course which is not going to be a part of the PhD degree they study for at UiT.

• PhD students from other institutions than UiT.

• Applicants that has achieved a master’s degree (five year master or three year bachelor degree + two year master degree), but isn’t a PhD student.

Admission requirements

• This course is aimed at PhD candidates from engineering disciplines, but is open to postdoc fellows if seats are available.
• PhD candidates from both UiT and NORDTEK universities may be admitted to the course. PhD candidates not registered at UiT must upload a document from their university stating that they are registered PhD candidates.

If there are 5 applicants or fewer, the course responsible will evaluate whether the course will take place.

Course content

Objectives of the course

Upon successful completion of the course, the following learning outcomes are expected to be achieved:

Knowledge:

After passing the course, the student is expected to be able to:

• Knowledge of concepts and definitions in the mathematics of modeling, numerical analysis, and optimization.
• Knowledge of formulating results covered by the course.
• Knowledge of theories in scientific computing cases.
• Understanding the solution methods in applied mathematics.

Skills:

After passing the course, the student is expected to be able to:

• Interpret concepts in scientific computing;
• Describe the main features (and proofs) of the main theorems;
• Apply the scientific computing methods to solve problems in applied mathematics.

Competence:

• After passing the course, the student is expected to be able to use computation to solve problems in engineering science by applying mathematical models.

Language of instruction and examination

Teaching methods

Lectures and problem-solving sessions.

Acquisition (face-to-face presentations and lectures, demos, reading paper books).

Investigation (using text-based study guides, analyzing the ideas and information in a range of materials & resources, searching and evaluating information and ideas, using digital resources for scaffolding, using digital tools to collect and analyze data).

Practice ("field" trips, using models, simulations, practicing exercises, doing practice-based projects).

Discussion (tutorials, seminars, class discussions, online tutorials)

Collaboration (individual project, online forums, wikis, chat rooms, etc.).

Production (reports, models, oral exam).