NeuroData is always hiring exceptional individuals at all levels. There are only a few requirements: (1) you are excited to work closely with a team of diverse thinkers, including computer scientists, biomedical engineers, and neuroscientists, (2) you are will to commit at least 2-3 years to the project, and (3) you understand that we do open science - this means that all code that we write is open sourced and therefore run by other people (one implication of this is that our code is always tested and documented). Assuming you fit those conditions, please read the below specialized instructions, and send us an email with your CV, your github handle, and other relevant information (such as recommendations and your first author publications that you are most proud of).
We are currently looking for 3 post-docs. In all cases, there will be a significant programming requirement, in either R or Python. Details for each position are below.
For the graph statistics postdoc, we will be (i) writing reference implementations in R for a number of graph statistical methods for which we do not currently have said implementations, (ii) developing new graph statistical theory and methods, and (iii) applying said methods to neuroscience data. All reference implementations will be incorporated into FlashX. We will write papers on graph statistics aimed at statistics and machine learning audiences, as well as some aimed at neuroscientists.
For the computational anatomy postdoc, we will be (i) writing code using the NeuroData infrastructure to extract neuroanatomical objects of interest (e.g., cells, synapses, regions), and (ii) developing and deploying methods for scalable statistical analyses for these objects, such as 3D point processes for 100 million points. Some of this work is explained in more detail here.
The human MRI postdoc will also be writing code using the NeuroData infrastructure, processing lots of open source brains, and making discoveries and writing papers using graph statistics, spatial statistics, and more. One example of this work is the MRI-to-Graphs pipeline, lovingly referred to as [m2g].
