[CogSci] job: machine learning research scientist at NIMH

[Francisco Pereira]

## HIRING: machine learning research scientist

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 leading federal agency for research on mental
disorders and neuroscience, and part of the National Institutes of Health
(NIH).

## About the NIMH Machine Learning Team

Our mission is to help NIMH scientists use machine learning methods to
address 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. Our
overarching goal is to use machine learning to improve every aspect of the
scientific effort, from helping discover or develop theories to generating
actionable results.

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 have excellent computational resources, both of our own
(tens of high-end GPUs for deep learning, several large servers) and shared
within the NIH (a cluster with hundreds of thousands of CPUs, and hundreds
of GPUs).

As a machine learning research group, we develop new methods and publish in
the main machine learning conferences (e.g. NeurIPS and ICLR), as well as
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. You can find more
about our work and recent publications at

https://cmn.nimh.nih.gov/mlt

## About the position

We are seeking candidates who are capable of combining machine learning,
statistical, and domain-specific computational tools to solve practical
data analysis challenges (e.g. designing experiments, generating and
testing statistical hypotheses, training and interpreting predictive
models, and developing novel models and methods). Additionally, candidates
should be capable of visualizing and communicating findings to a broad
scientific audience, as well as explaining the details of relevant methods
to researchers in a variety of domains.

Desirable experience that is not required, but will be considered very
favorably:

- deep learning
- reinforcement learning
- Bayesian statistical modelling
- other types of modelling of human/animal learning and decision-making
- neuroimaging data processing/ analysis (any MRI modality, MEG, or EEG)
- other types of neural data (e.g. neural recording, calcium imaging)

in the context of substantial research projects, ideally having led to
submitted or published articles.

Finally, you should have demonstrable experience programming in languages
currently used in data-intensive, scientific computing, such as Python,
MATLAB or R. Experience with handling large datasets in high performance
computing settings is also very valuable. Although this position requires a
Ph.D. in a STEM discipline, we will consider applicants from a variety of
backgrounds, as their research experience is the most important factor.
Backgrounds of team members include computer science, statistics,
mathematics, and biomedical engineering.

This is an ideal position for someone who wants to establish a research
career in method development and applications driven by scientific and
clinical needs. Given our access to a variety of collaborators and large or
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 or community impact.

If you would like to be considered for this position, please send
francisco.pereira at nih.gov a CV, with your email serving as cover letter. We
especially encourage applications from members of underrepresented groups
in the machine learning research community. If you already have a research
statement, please feel free to send that as well. There is no need for
reference letters at this stage. Other inquiries are also welcome. Thank
you for your attention and interest!
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://lists.cognitivesciencesociety.org/pipermail/announcements-cognitivesciencesociety.org/attachments/20211101/8bbf6285/attachment.html>