[Comp-neuro] position: machine learning research scientist at NIMH
francisco.pereira at gmail.com
Fri Feb 15 18:55:16 CET 2019
The Machine Learning Team at the National Institute of Mental Health (NIMH)
in Bethesda, MD, has an open position for a machine learning research
scientist. The NIMH is the lead federal agency for research on mental
disorders, and part of the the National Institutes of Health (NIH).
Our mission is to help NIMH scientists use machine learning methods to
address a diverse set of research problems in clinical and cognitive
psychology and neuroscience. These range from identifying biomarkers for
aiding diagnoses to creating and testing models of mental processes in
healthy subjects. We work with many different data types, including very
large brain imaging datasets from various imaging modalities, behavioral
data, and picture and text corpora. We also develop new machine learning
methods and publish in the main machine learning conferences (e.g. NeurIPS
and ICLR), and in psychology and neuroscience journals. Many of our
problems require devising research approaches that combine imaging and
non-imaging data, and leveraging structured knowledge resources (databases,
scientific literature, etc) to generate explanations and hypotheses.
We have excellent computational resources, both of our own (tens of GPUs
for deep learning) and shared within the NIH (a top-100 supercomputer with
hundreds of thousands of CPUs, and hundreds of GPUs). You can find more
about our work and publications at https://cmn.nimh.nih.gov/mlt.html.
This is an ideal position for someone who wants to establish a research
career in methods development and applications driven by scientific and
clinical needs. Given our access to a variety of collaborators and
large/unique datasets, there is ample opportunity to match research
interests with novel research problems. We also maintain collaborations
outside of the NIH, driven by our own research interests.
We are seeking candidates with practical machine learning experience (e.g.
training of classification and regression models, statistical testing of
results, interpretation and visualization of key aspects of models). Beyond
this, the ideal candidate would have knowledge of optimization and
statistics, insofar as they bear on machine learning methods development.
Experience working with neuroimaging data (any MRI modality, as well as
MEG/EEG) will be considered very favorably, but is not required. Finally,
you should have demonstrable experience coding in languages currently used
in data-intensive, scientific computing such as Python, MATLAB or R.
If you would like to be considered for this position, please send
francisco.pereira at nih.gov a CV, with your email as cover letter; if you
have a research statement, please feel free to send that as well. Other
inquiries are also welcome. Thank you for your interest!
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