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 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. 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, e.g. very large brain imaging datasets in various imaging modalities, neural recordings, 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

About the position

This position requires experience in the use of deep learning in the context of substantial research projects, ideally having led to publications (or preprints). As our team works on both applications and method development, here are some examples of projects we have carried out or are presently engaged in:

• Bayesian deep neural networks for brain segmentation with uncertainty
• convolutional neural networks on structural or functional brain MRI for decoding information or person characteristics
• a comparison of approaches for generating gradient-based saliency maps for neural networks in brain imaging data
• a method for distributed training and consolidation of Bayesian deep neural networks
• modifications of neural network models of vision to test hypotheses about visual representations in the brain
• transformer models for predicting fine-grained content labels in text transcripts from therapy sessions
• improving transfer learning in neuroimaging
• fine-tuning of large language models for emulation of participants in psychology experiments

In general, 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.

Other desirable experience includes:

• mathematical optimization (e.g. convex, linear programming, integer programming)
• statistical inference (e.g. generalized linear models, mixed effect models, state space models, survival analysis)
• 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)
• modelling of other types of neural data (e.g. neural recordings, calcium imaging)

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 methods 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@nih.gov a CV, with your email serving as a 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!