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Postdoctoral Fellow - Stanford, United States - Stanford University
Description
A causal 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 the integration of mental health, physical health, immune fitness, and nutrition in various clinical settings.
Particular areas of interest include pregnancy and neonatology. Our group has a strong commitment to translating research findings into 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. Application information:
Relevant background:
-Ph.D. or Postdoctoral Fellow in a quantitative field with research experience in building/applying causal machine learning models
-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.
Experience applying/developing counterfactual models or causal discovery models
Experience modeling confounders
Track record in development of open-source software adopted by the research community.
Keywords:
Causal Machine Learning, Causal Inference, Causal Discovery, Counterfactual, Causal Deep Learning, Causal Structure, Structure Learning, Structure Equation Model, SEM, Explainable AI, XAI, Data Science, Deep Learning, R, Python, Julia, Bioinformatics, Multiomics, Integrative Analysis, Medicine, Actigraphy, Pregnancy, Precision Medicine, Personalized Medicine, EHR, Electronic Health Records.