Dept ID:
BIODATSCI
Open Postdoctoral position, faculty mentor Vivek Charu
We are seeking a postdoctoral fellow to work on cutting-edge single-cell and spatial-omics research. The ideal fellow will be interested in developing and applying novel computational algorithms to novel datasets generated in the setting of non-neoplastic and neoplastic disease.
Open Postdoctoral position, faculty mentor Vivek Charu
We are generating spatial transcriptomic/multi-omic data on neoplastic and non-neoplastic diseases. We are hoping to recruit a postdoctoral fellow to lead the analysis of multiple disease-specific datasets and contribute to the development of novel methodologies in this space. The ideal applicant will have a strong background in bioinformatics methods and a keen interest in translational science. The postdoctoral fellow will work closely with Dr. Vivek Charu and Dr. Brooke Howitt.
Olivier Gevaert
Multi-omics, multi-modal, multi-scale data fusion for precision medicine
Aaron Newman
Our group combines computational and experimental techniques to study the cellular organization of complex tissues, with a focus on determining the phenotypic diversity and clinical significance of tumor cell subsets. We have a particular interest in developing innovative data science tools that illuminate the cellular hierarchies and stromal elements that underlie tumor initiation, progression, and response to therapy.
Olivier Gevaert
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.
Andrew Gentles
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.
Julia Salzman
Statistical algorithms for genomics, RNA biology, splicing, cancer genomics, spatial transcriptomics
Nigam Shah
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.
Olivier Gevaert
Multi-omics, multi-modal, multi-scale data fusion in complex diseases using machine learning