Roles
Application Note
The courses will be run at the same time so you can only be accepted into one role/course (which will be based on the priority you indicate in your profile). For example, if you aren’t accepted to your top choice, we will evaluate your application to your second choice.
Below is just a brief description of the roles.
Mentors
Mentors are a valuable aspect of summer schools in Computational Neuroscience and Deep Learning
They provide networking opportunities and can influence up-and-coming researchers' careers
Mentors will be matched with student groups based on research interests and meet with them for 4 meetings of approximately 30 minutes over 3 weeks (July 10-28)
Mentors do not need to prepare anything before the meetings and may mentor more than one group if desired.
Students
Expectations for students:
Courses are intensive and require a full-time commitment.
8 hours of study per day, 5 days a week.
Students are placed in pods with about 15 students and a teaching assistant.
Work on research projects with input from teaching assistants and mentors.
Taught through Google Colab or Kaggle using Python.
Support is provided to help students succeed.
Expertise required:
Knowledge in coding (Python)
Mathematics
Statistics
We offer a pre-course to help you brush up on your Python skills.
Timeline:
Acceptance emails are sent to prospective students within about one month.
Deadlines and course dates can be found in the course descriptions on the website.
Missing several days of the course may result in no certificate being granted.
To enroll, students should be able to attend the entire course with no more than 1-2 days of absence.
If conflicts prevent this, enrollment should be postponed to a later year.
Certificates of completion:
Students who complete the course and/or the project component will receive a personalized certificate of completion.
Teacher Assistants
Responsibilities of Teaching Assistants
We have 3 types of Teaching Assistants:
1- Regular Teaching Assistants
Guide students through daily tutorials and assist in their research projects.
Responsible for a pod of students (~15).
Direct video contact during the 4.5 hours.
Daily 1.5 hours to help students with their projects.
2- Project Teaching Assistants
Guide and help several project groups (~7).
Specialize in at least one of the data sets or projects that students can work on.
Typically meet with each of their groups every 1-2 days for 1-2h at a time.
3- Lead Teaching Assistants
Have additional leadership responsibilities, such as advising teaching assistants, managing teaching assistant absences, and guiding less experienced TAs on content.
Have to dedicate an extra week beyond the regular TA preparation to become familiar with several days of NMA content.
All Teaching Assistants will undergo teaching assistant training, which includes material to prepare beforehand and attending two sessions before the course.
Expectations from Teaching Assistants
Teaching assistants facilitate problem-based learning and provide support and guidance to students
They aim to promote critical thinking, growth, and engagement in students
Compensation and Benefits for Teaching Assistants
Teaching assistants who are eligible will receive a stipend
All teaching assistants will receive a teaching letter of recommendation
They have access to training material and can ask content questions during the course
They can deepen their knowledge and ask questions directly to world-expert lecturers
Expertise and Requisites Required for Teaching Assistants:
Fundamental background in the course topic (computational neuroscience, deep learning, or climate science).
Proficiency in Python.
Completion of an undergraduate degree.
Have strong verbal and written communication skills in English.
Acceptance of the terms of agreement and time commitment.