NeuroHackademy 2019


#1

We are happy to announce a call for applications to participate in Neurohackademy 2019!

This two-week hands-on workshop held at the University of Washington eScience Institute in Seattle, July 29th - August 9th, 2019, focuses on tools and techniques used to analyze human neuroscience data, on methods used to extract information from large datasets of publicly available data (such as the Human Connectome Project, OpenfMRI, etc.), and on tools for making human neuroscience research open and reproducible.

Neurohackademy sessions in the first week will include lectures and tutorials on data science, machine learning, data visualization and data resources. The second week will be devoted to participant-directed activities: guided work on team projects, hackathon sessions, and breakout sessions on topics of interest.

For more details and a preliminary list of instructors, see: https://neurohackademy.org/

We are now accepting applications to participate at: https://neurohackademy.org/apply/

Ideally, applicants should have some prior experience with programming and with neuroscience data analysis, but we welcome applications from participants with a variety of relevant backgrounds.

Accepted applicants will be asked to pay a fee of $200 upon final registration. This fee will include participation in the course, accommodation in the UW dorms, and two meals a day (breakfast and lunch), for the duration of the course. A limited number of fee waivers and travel grants will be available. We encourage students with financial need and students from groups that are underrepresented in neuroimaging and data science to apply for these grants (see application form for details).

Important dates:

February 18th: Application deadline

March 15th: Notification of acceptance

April 1st: Final registration deadline


#2

Hi,this is great.
I’d like to know what were some of the minimum qualifications of previously accepted applicants?Is this mostly for research students pursuing PhD degrees in neuroscience analysis?What factors are looked upon favorably?


#3

Thanks for asking.

I recommend taking a look at our FAQ

To answer some of your specific questions, we have had a large variety of participants. Typically about half are graduate students, a quarter are post-docs and a quarter are other categories, including faculty, research staff and even a couple of non-academics (working for companies that analyze neuroscience data). This also reflects the abundance of these different categories in the applicant pool.

In terms of things we look for in an application: we look for some experience with programming (enough to benefit from the experience), though we don’t require experience in any particular technology. We usually want people to have some experience with neuroscience data, though we would also consider applications from researchers in data science fields (e.g., statistics, CS, etc.), if the application explains what the objective of training is. We like to see people state why they think the training would be beneficial (to their career, training, etc.).

These criteria tend to favor early-career academic researchers (PhD students and postdocs), but not necessarily. For example, junior faculty have been able to make compelling arguments that training in data science would be beneficial to them in a transition point in their research program. Same for researchers in industry.