Computational Science

The student learns to use modelling and simulation techniques in science and engineering, with special emphasis on nanobiology. The students first become familiar and/or improve their existing skills with the programming environment of Python. The students work then on assignments (individually or in groups) where they learn different numerical methods and their implementation in Python. At the end of the course the students use computer modelling in order to analyse and solve a scientific problem related to nanobiology.

1. The assessment consists of a mixture of quizzes, assignments and an oral exam (in groups).

1.a There is no written exam (this is a practical)

2. The resit consists of a longer interview (about 15') that will also cover the content of the quizzes.

2.a the resit of the interview is scheduled for week Q4.8

3. The final grade is composed as follows:  

• Deliverables (50%)

  • 4 quizzes (submitted online, with attendance code) covering soft computing knowledge (1st week of projects 1, 2, 3 and 4) 

  • 4 graded assignments (paper, in class) testing theoretical and computational knowledge (2nd week projects 1, 2, 3 and 4)

  • Total number of deliverables = 8. The best 6 will count towards the “Deliverables” grade

• Final Project (50%)

  • Peer feedback assignment (10%)

  • Final project interviews (40%)

4. Registration for oral exam is done via Brightspace (find instructions and deadlines in "Course Information").

5. Students needing to turn work in late due to personal circumstances must contact the Academic Counsellor prior to turning it in to make arrangements. Other late work might not be graded.