The postdoctoral researcher will conduct methodological research at the intersection of AI reasoning, biomedical informatics, and clinical medicine. Our work focuses on understanding how AI systems reason over complex longitudinal patient information, how reasoning can be combined with data-driven models of patient trajectories, and how we can rigorously determine when AI-generated clinical decisions are reliable.
Vivek Charu and Manisha Desai invite applications for a postdoctoral position focused on clinical trial design and advanced causal inference methods. The successful candidate will contribute to potentially several studies, working collaboratively on methodological development and applied research.
Applicants should hold a PhD in statistics, biostatistics, computer science, computational biology, or a closely related field and have demonstrated interest and expertise in causal inference and clinical trial design.
Multi-omics, multi-modal, multi-scale data fusion for precision medicine
Vast amounts of molecular data characterizing the genome, epi-genome and transcriptome are becoming available for a wide range of complex disease such as cancer and neurodegenerative diseases. In addition, new computational tools for quantitatively analyzing medical and pathological images are creating new types of phenotypic data. Now we have the opportunity to integrate the data at molecular, cellular and tissue scale to create a more comprehensive view of key biological processes underlying complex diseases.
Our research focus is in computational systems biology, primarily in cancer and more recently in neurodegenerative diseases. We develop and apply methods to understand biological processes underlying disease, using high-throughput genomic and proteomic datasets and integrating them with phenotypes and clinical outcomes. A key interest is dissecting how the cellular composition and organization of tissues affects their behaviour in disease; and how these things might be targeted for therapy or diagnostic purposes.
We analyze multiple types of health data (EHR, Claims, Wearables, Weblogs, and Patient blogs), to answer clinical questions, generate insights, and build predictive models for the learning health system. Our group runs the country's only bedside consult service to enable better medical decisions using aggregate EHR and Claims data at the point of care. Our team leads the Stanford Medicine Program for Artificial Intelligence in Healthcare, which makes predictions that allow taking mitigating actions, and studies the ethical implications of using machine learning in clinical care.
Multi-omics, multi-modal, multi-scale data fusion in complex diseases using machine learning