A key aim of this school is to foster close collaboration and camaraderie between students, teaching assistants, and faculty. Students will be working hard on their projects and taking part in many social activities with each other and the faculty. For this reason, all students will be required to stay in the student accommodation, attend all meals and activities etc. This also means costs are essentially the same for all students. Note though that bursaries and travel grants will be provided to as many students as possible, on a basis of need and merit. In your application, specify what financial contribution you can make and be sure to request financial assistance where asked.
The format of the school will be a combination of intensive lectures on advanced topics in computational and theoretical neuroscience as well as practical exercises in simulation and data analysis. In addition, students will perform a mini-research project under the supervision of one of the school tutors, to be presented at the end of the school.
The total fee for the Imbizo is EUR 1,350.00.
Thanks to our generous sponsors, significant financial assistance is available to reduce and waiver fees for students, as well as to provide some travel bursaries. If you need financial assistance to attend the Imbizo, please state so clearly in the relevant section of your application.
NOTE: we cannot guarantee full financial assistance to non-resident African or non-African applicants. African students are particularly strongly encouraged to apply.
Find our FAQ here.
Has a bachelors in physical sciences (e.g. physics / statistics / mathematics / computer science). Has taken 1st-year level courses in biology / electives in psychology, etc.
Has a bachelors in biological science, has completed 2nd-year level courses in statistics / applied mathematics / bioinformatics. Now in Masters/PhD. Has some experience writing code.
This school will be most beneficial for postgraduate students and postdocs who want to fast track their education in the quantitative aspects of neuroscience. If you already have a strong background in Computational Neuroscience or Machine Learning, perhaps you would be better suited as a Teaching Assistant?