Deep Learning Eeg Jobs (NOW HIRING)

The Department of Neurology & Neurological Sciences at Stanford University School of Medicine is building a world-class program at the intersection of artificial intelligence and brain health. The laboratory of Dr. M. Brandon Westover develops and deploys AI systems that interpret brain data at scale - EEG, sleep studies, wearable recordings, neuroimaging, and the electronic health record - to improve diagnosis and treatment in epilepsy, neurocritical care, sleep medicine, and neurology broadly.

We are seeking a Research and Development Scientist and Engineer 2 to serve as a senior research scientist leading scientific work across this program. Where our engineering staff build the infrastructure, you will drive the science: framing the questions, designing the studies, developing and validating the models, and publishing the results. You will work with one of the largest curated collections of clinical neurophysiology data assembled anywhere, spanning EEG, polysomnography, wearable monitoring, imaging, and linked electronic health records.

The role is deliberately broad. You will lead your own lines of investigation, guide models from prototype through rigorous validation toward clinical deployment, provide scientific direction to postdoctoral fellows and students, contribute to grant proposals, and help shape the research agenda of the emerging Stanford Neurology AI Center.

This position is offered as a hybrid role (on-site at Stanford’s main campus, Center for Academic Medicine - 453 Quarry Rd., three days per week and telecommuting two days per week), subject to operational needs.

• PhD preferred, in biomedical informatics, computer science, electrical or biomedical engineering, neuroscience, statistics, epidemiology, or a related field.
• Master's degree with commensurate research experience will be considered.
• Five or more years of relevant research experience, including independent leadership of research projects from question formulation through publication.
• Strong record of peer-reviewed publications applying machine learning or advanced statistical methods to biomedical, physiological, or clinical data.
• Deep expertise in machine learning and deep learning for time-series or signal data; strong proficiency in Python and modern frameworks such as PyTorch.
• Experience with EEG, polysomnography, or other neurophysiological data strongly preferred.
• Experience with large-scale electronic health record data, causal inference, or development and external validation of clinical prediction models.
• Demonstrated experience contributing to competitive grant proposals; prior success as a named investigator desirable.
• Experience mentoring or supervising junior scientists, students, or engineers.
• Working knowledge of translational and regulatory pathways for clinical AI (e.g., FDA Software as a Medical Device) desirable.
• Excellent scientific writing and presentation skills, and the ability to work effectively across clinical, engineering, and data science teams.

• Frequently grasp lightly/fine manipulation, perform desk-based computer tasks, lift/carry/push/pull objects that weigh up to 10 pounds.
• Occasionally stand/walk, sit, twist/bend/stoop/squat, grasp forcefully.
• Rarely kneel/crawl, climb (ladders, scaffolds, or other), reach/work above shoulders, use a telephone, writing by hand, sort/file paperwork or parts, operate foot and/or hand controls, lift/carry/push/pull objects that weigh >40 pounds.

* - Consistent with its obligations under the law, the University will provide reasonable accommodation to any employee with a disability who requires accommodation to perform the essential functions of his or her job.

• May be exposed to high voltage electricity, radiation or electromagnetic fields, lasers, noise > 80dB TWA, Allergens/Biohazards/Chemicals /Asbestos, confined spaces, working at heights 10 feet, temperature extremes, heavy metals, unusual work hours or routine overtime and/or inclement weather.
• May require travel.

• Interpersonal Skills: Demonstrates the ability to work well with Stanford colleagues and clients and with external organizations.
• Promote Culture of Safety: Demonstrates commitment to personal responsibility and value for safety; communicates safety concerns; uses and promotes safe behaviors based on training and lessons learned.
• Subject to and expected to comply with all applicable University policies and procedures, including but not limited to the personnel policies and other policies found in the University's Administrative Guide, http://adminguide.stanford.edu.