Postdoctoral Researcher in Machine Learning and Biology

1 month ago


Palo Alto, California, United States Stanford University Full time
Postdoctoral Researcher in Machine Learning and Biology

Stanford University School of Medicine is seeking a highly motivated postdoctoral researcher to join our machine learning group. As a postdoctoral scholar, you will have the opportunity to work with world-experts in artificial intelligence, biology, and medicine to develop innovative solutions for real-world problems.

Our research focuses on integrating machine learning with clinical decision making, with a particular emphasis on pregnancy and neonatology, recovery from stroke and surgery, and physical and biological aging. We encourage our postdoctoral fellows to receive extensive training in entrepreneurship and business management from the Stanford University Graduate School of Business.

We are committed to diversity and inclusion, and welcome applicants from groups traditionally underrepresented in computer science and machine learning.

Responsibilities
  • Develop and apply machine learning models to real-world problems in medicine and biology
  • Collaborate with our team of experts to design and implement innovative solutions
  • Contribute to the development of open-source software and publications in leading AI/ML/Bioinformatics conferences and journals
Requirements
  • PhD in a quantitative field with research experience in building/applying machine learning models
  • Excellent publication and external funding track record
  • Interest in medicine and biology
  • Familiarity with modern AI/ML platforms and libraries such as PyTorch, TensorFlow, or Jax
Preferred Qualifications
  • 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
Keywords

Machine Learning, Data Science, Deep Learning, Multitask Learning, Transfer Learning, Representation Learning, Graph Learning, Foundation Models, Clustering, Classification, Data 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, Precision Medicine, Personalized Medicine, EHR, Electronic Health Records, Wearable Devices



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