A machine learning and bioinformatics postdoctoral scholar position is available at the Stanford University School of Medicine. No prior life science experience is necessary.
The scholar will join a bioinformatics group with unparalleled direct access to clinical resources, as well as Stanford’s world-experts in machine learning and immunology. This is a unique opportunity for a machine learning scientist to directly impact patients’ lives in a clinical setting. Our research covers a wide range of unconventional yet high-impact topics ranging from space medicine to integration of mental health, physical health, immune fitness, and nutrition in various clinical settings. We are primarily a computational immunology research group but depending on the problem at hand, our datasets include clinical measurements, readouts from advanced wearable technologies, and various genomics, proteomics, single cell, microbiome, and imaging assays, often in a multiomics and multicohort setting.
Our group has a strong commitment to translating research findings to actionable products. We encourage (and financially support) our postdoctoral fellows to receive extensive training in entrepreneurship and business management from Stanford’s School of Business. This is an excellent opportunity for a candidate who is not only interested in participating in state-of-the-art academic research, but is also interested in exploring industrial and entrepreneurial career trajectories.
Keywords: Machine Learning, Data Science, Deep Learning, Clustering, Classification, Visualization, Bioinformatics, R, Python, Systems Biology, Immunology, Multiomics, Integrative Analysis, Medicine, Single Cell Biology, Cytometry, Flow Cytometry, Mass Cytometry, CyTOF, Proteomics, Actigraphy, Pregnancy, Trauma, Surgery, Stroke, Space Medicine, Precision Medicine, Personalized Medicine.
-Ph.D. in a quantitative field
-Strong programming and statistics background
-Excellent publication and external funding track record
-Interest (but not necessarily expertise) in medicine and biology
For more information please visit: nalab.stanford.edu
To apply, please submit a CV and a brief cover letter to email@example.com
We highly value our laboratory's level of diversity. Applicants from groups traditionally underrepresented in computer science and machine learning are strongly encouraged to apply.