Openings in DBDS

Postdoctoral Positions in Cancer Genomics/Computational Biology

Stanford Institute for Stem Cell Biology and Regenerative Medicine (SCBRMI)

The Newman Lab, in the Department of Biomedical Data Science at Stanford University, is seeking to recruit creative and driven postdoctoral fellows interested in working at the intersection of biomedical data science and cancer/stem cell biology. A major goal of the lab is the development of innovative computational methods that illuminate the cellular hierarchies and stromal elements that underlie tumor initiation, progression, and response to therapy. Recent work from our group includes papers describing CytoTRACE (Gulati et al., Science 2020) and CIBERSORTx (Newman et al., Nature Biotechnology 2019). We are also interested in devising approaches that address pressing analytical needs related to emerging genomic technologies. Successful applicants will be expected to develop and/or apply computational tools to address basic or clinical research questions within our areas of focus, including tumor differentiation and development, the cellular composition of the tumor microenvironment, and cell lineage relationships in malignant and normal tissues. Key results are further explored experimentally, both in our lab and through collaboration, with the ultimate goal of translating promising findings into the clinic.

Two Open Postdoctoral Positions in Rivas lab

Rivas Lab is recruiting: 1) A postdoctoral fellow to work on the genetics of common diseases across multiple disease efforts and population cohorts. We will be leveraging genome and exome sequencing data from multi-ethnic populations. Furthermore, we will be developing methods for the analysis of rare variants and their relevance to complex traits. 2) A postdoctoral fellow to work on the genetics of disease progression. This work will be done with datasets from multiple cohorts including the UK Biobank and FinnGen. The research will include novel methods development and application with large disease datasets.

Qualifications: Bachelor of Science, PhD; Python and R

Required Application Materials: CV, Letters of Recommendation, and Publication Records

Send questions to Dr. Manuel Rivas at 

Learn more about the Rivas Lab at:

Open Postdoctoral Scholar Position in Medical AI

Department of Biomedical Data Science
Stanford University School of Medicine

The Laboratory of Quantitative Imaging and Artificial Intelligence (QIAI) in the Department of Biomedical Data Science at Stanford University is searching for a postdoctoral scholar. The QIAI Laboratory is led by Dr. Daniel Rubin, who is also affiliated with the Departments of Radiology and Medicine (Biomedical Informatics Research) at Stanford University. The lab focuses on cutting‐edge research at the intersection of imaging science and biomedical informatics, developing and applying AI methods to large amounts of medical data for biomedical discovery, precision medicine, and precision health (early detection and prediction of future disease). The lab develops novel methods in text and image analysis and AI, including multi-modal and multi-task learning, weak supervision, knowledge representation, natural language processing, and decision theory to tackle the challenges of leveraging medical Big Data. Our exciting work is bridging a spectrum of biomedical domains with multidisciplinary collaborations with top scientists at Stanford as well as with other institutions internationally.

The QIAI lab provides a unique multidisciplinary environment for conducing innovative AI-based healthcare research with a strong record of scholarly publication and achievement. Core research topics in the laboratory include: (1) automated image annotation using unsupervised methods of processing associated radiology reports using word embeddings and related methods; (2) developing methods of analyzing longitudinal EMR data to predict clinical outcomes and best treatments, (3) creating multi-modal deep learning models integrating multi-dimensional EMR and other data to discover electronic phenotypes of disease, (4) developing AI models with noisy or sparse labels (weak supervision), and cross-modal, multi-task learning, and observational AI approaches, and (5) developing and implementing algorithms for distributed computation for training deep learning models that leverage multi-institutional data while avoiding the barriers to data sharing.

The postdoctoral scholar will be working on two core research topics: (1) develop foundational AI methods for analyzing and extracting information from clinical texts; (2) develop clinical prediction models using multi-modal and longitudinal electronic medical records (EMR) data. The scholar will deploy and evaluate these methods as clinical applications to transform medical care.