Machine learning research scientist at NIMH

Machine Learning Research Scientist – National Institute of Mental Health (NIMH)

The Machine Learning Team at the National Institute of Mental Health (NIMH) in Bethesda, MD, is seeking a Machine Learning Research Scientist. Our mission is to help NIMH scientists use machine learning methods to address research problems in clinical and cognitive psychology and neuroscience.

We work with many different data types, including large brain imaging datasets, behavioral data, and text corpora. We provide computational resources such as high‑end GPUs, large servers, and NIH clusters. Our group develops new methods and publishes in top machine learning and neuroscience venues. Many of our problems require combining imaging and non‑imaging data and leveraging structured knowledge resources to generate explanations and hypotheses.

• Develop and apply machine learning methods to solve practical data‑analysis challenges (e.g., designing experiments, generating and testing statistical hypotheses).
• Train and interpret predictive models and develop novel models and methods.
• Visualize and communicate findings to a broad scientific audience.
• Explain detailed methods to researchers across domains.
• Collaborate with interdisciplinary teams and external partners.

Applicants must hold a Ph.D. in a STEM discipline. Preference will be given to applicants who have a strong research background, demonstrated experience programming in Python, MATLAB, or R, and experience handling large datasets in high‑performance computing environments.

Desirable but not required:

• Deep learning
• Reinforcement learning
• Models of human/animal learning and decision‑making
• Neuroimaging data processing/analysis (MRI, MEG, EEG)
• Other neural data (neural recording, calcium imaging)

These skills should have been applied in substantial research projects, ideally resulting in submitted or published articles.

This is an ideal position for someone who wants to establish a career in method development and applications driven by scientific and clinical needs. Given our access to diverse collaborators and datasets, there is ample opportunity to match research interests with novel problems.

To apply, email francisco.pereira a CV and cover letter using your email address. We especially encourage applications from underrepresented groups in the machine learning research community. A research statement is optional; reference letters are not required at this stage.