A machine learning-focused postdoctoral scholar position is available at the Stanford University School of Medicine. No prior life science experience is necessary. The scholar will join a machine learning group with unparalleled direct access to clinical resources, as well as Stanford’s world-experts in artificial intelligence, biology, and medicine. 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. Particular areas of interest include pregnancy and neonatology, recovery from clinical challenges including stroke and surgery, as well as physical and biological aging.
Our group has a strong commitment to translating research findings to actionable insights and products with real-world scalability. 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.
Diversity across all dimensions is not only a core value for our laboratory, but also is a key contributor to our innovative research. Applicants from groups traditionally underrepresented in computer science and machine learning are strongly encouraged to apply.
- To receive full consideration, please apply using the following google form: https://docs.google.com/forms/d/e/1FAIpQLSdgPBJi028fNIVrbXrXFhDXRbc0gXeIN8wcHjQKKiObPJDmNA/viewform?usp=sf_link
- Questions can be directed to firstname.lastname@example.org
- For more information please visit: https://nalab.stanford.edu
-Ph.D. in a quantitative field with research experience in building/applying machine learning models during PhD/industry or postdoctoral experience in Machine Learning
-Excellent publication and external funding track record
-Interest (but not necessarily expertise) in medicine and biology
-Familiarity with modern AI/ML platforms and libraries such as PyTorch, TensorFlow, and Jax.
Preferred, but not mandatory :
-History of publications in leading AI/ML/Bioinformatics conferences and journals.
-Diverse experience in varied AI/ML concepts.
-Track record in development of open-source software adopted by the research community.
-Experience in medicine and/or biology.
Machine Learning, Data Science, Deep Learning, Multitask Learning, Transfer Learning, Clustering, Classification, Visualization, R, Python, Julia, Bioinformatics, Systems Biology, Immunology, Single Cell Analysis, Multiomics, Integrative Analysis, Medicine, Single Cell Biology, Cytometry, Flow Cytometry, Mass Cytometry, Proteomics, Metabolomics, Genomics, Sequencing, Human Activity Monitoring, Actigraphy, Pregnancy, Trauma, Surgery, Stroke, Space Medicine, Precision Medicine, Personalized Medicine, EHR, Electronic Health Records, Wearable Devices.