Application Period: April 10, 2019 - April 21, 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, coming out of a PhD program or postdoc, 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 firstname.lastname@example.org a CV, with your email serving as a cover letter; if you have a research statement, please feel free to send that as well. Applications received by April 21 will receive full consideration, but the position will remain open until it is filled. Other inquiries are also welcome. Thank you for your interest!