Sort by
Refine Your Search
-
interpreting vast datasets, including claims data, electronic health records, and other publicly accessible datasets – all with a keen focus on pediatric pain, sleep, opioids, and perioperative outcomes
-
on manuscripts, presentations, and research proposals Required Qualifications: PhD in psychology, neuroscience, biostatistics, computer science, or a related field. Strong interpersonal and technical skills
-
, Stanford PRL), career development resources, and competitive benefits and salary commensurate with experience. Required Qualifications: PhD in physics, electrical engineering, mechanical engineering
-
is $76,383. Are you looking for a challenging and rewarding postdoctoral fellowship in pain science, substance use disorders (SUD), or data science? Join the next generation of pain and SUD
-
patients. You’ll use cloud computing and modern data science tools to analyze high-dimensional, time-resolved data from clinical environments. You’ll collaborate with faculty in AI, clinical informatics, and
-
the field of dermatology, leveraging epidemiology, data science, and public health to advance health equity. Leandra A. Barnes, MD is an NIH-funded K scholar committed to elucidating the underlying mechanisms
-
-AI), and retrieval-augmented generation (RAG). Tina Hernandez-Boussard, MS, MPH, PhD (link is external) , is Professor of Medicine (Biomedical Informatics), of Biomedical Data Science, of Surgery and
-
. This position will be located in the Center's Cancer Cell Therapy Data Hub, which is led by Dr. Zinaida Good and provides a collaborative environment that pushes the cutting edge of technology to learn from
-
merits have been published in the premier science and technical journals. The group is well funded by NIH and industry partners. For more information, please visit http://med.stanford.edu/Gulab (link is
-
in Neuroscience, Biomedical Engineering, Computational Biology, or a related field. Strong background in signal processing, including neuroimaging and/or electrophysiology (EEG, MEG) data analysis