A postdoctoral fellowship is available at the Stanford University School of Medicine focused on implementing machine learning on population-scale clinical and biological data to characterize biological aging, resilience, and rejuvenation.
This is a unique opportunity to apply ML in unconventional and paradigm-shifting research with the potential to directly and broadly impact human lives. By joining our research group, you will:
- Have direct access to unparalleled population-scale clinical and biological data sources, along with the computational resources required to analyze these data and develop sophisticated machine learning models.
- Work in a highly collaborative environment with researchers from diverse backgrounds in medicine, biological sciences, and computer science.
- Collaborate with world-leading experts at Stanford University across the spectrum of AI, medicine, and biology.
- Be part of an environment strongly committed to translating research findings into actionable insights and scalable real-world products. We encourage (and financially support) our postdoctoral fellows to pursue extensive training in entrepreneurship and business management through Stanford’s Graduate School of Business. This position is well suited for candidates interested not only in state-of-the-art academic research, but also in exploring industrial and entrepreneurial career trajectories.
As a Stanford postdoctoral scholar, you will receive a highly competitive salary and comprehensive benefits, including best-in-class health and dental insurance. You will also receive priority consideration for Stanford housing and have access to Stanford’s broad range of professional development, learning, and growth resources.
Relevant background:
- MD or PhD, with research experience building and/or applying machine learning models during graduate studies, industry, or postdoctoral work.
- Excellent publication record.
- Interest in medicine and biology.
- Familiarity with modern AI/ML platforms and libraries (e.g., PyTorch and/or TensorFlow), traditional machine learning frameworks (e.g., scikit-learn), and statistical analysis in Python.
Preferred, but not required:
- Familiarity with electronic health record (EHR) data and cohort/outcome definition using EHRs.
- Experience in medicine and/or biology, particularly in aging-related research and omics-based analyses.
- Broad experience across diverse AI/ML concepts, including causal inference.
- Publications in leading AI/ML, biology, or medicine conferences and journals.
Keywords:
Machine Learning, Precision Medicine, Biology, Omics, Proteomics, metabolites, Electronic Health Records, Artificial Intelligence, Data Science, Deep Learning, Bioinformatics
How 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 naghaeep@stanford.edu
- For more information please visit: https://nalab.stanford.edu
