Job Opportunities

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.


·       Post-graduate degree (PhD or MD, completed or near completion) in biomedical data science, informatics, computer science, engineering, statistics, computational biology, or a related field

·       Experience in machine learning and AI, particularly in computer vision and image analysis

·       Strong record of distinguished scholarly achievement

·       Outstanding communication and presentation skills with fluency in spoken and written English

To Apply:

Interested applicants should submit a Curriculum Vitae, a brief statement of research interests, and three letters of reference in one PDF document to rubin‐lab‐

Lab Web page

Stanford University is an affirmative action, equal opportunity employer.

Stanford University Cluster Hire

5 Full-Time Staff Positions Available in Programming and Software Development for Curation in Clinical Genomics

Institution: Stanford University

Department: Biomedical Data Science, Stanford School of Medicine

Group: Clinical Genome Resource (ClinGen) Team; Laboratory of Dr. Carlos Bustamante

Location: Flexible (remote work possible); ideally able to travel to Palo Alto, CA for meetings

Cluster Description: The Clinical Genome Resource (ClinGen, is a multi-institutional consortium funded by the NIH National Human Genome Research Institute (NHGRI), dedicated to curating the human genome. The Stanford ClinGen team is focused on building the informatics backbone of ClinGen, in collaboration with teams at other institutions.

We are seeking new members of our team who are highly experienced in programming and are motivated to work as part of a collaborative effort within Stanford and across institutions.

We are growing the size of our team three-fold in order to meet the demands of our curation partners (users) who are working at the intersection of clinical care and genomics research. All qualified candidates will be expected to contribute to all phases of software development at ClinGen, including systems analysis, application design, development, implementation, modification, and maintenance. Our stack includes Python (Pyramid), ReactJS, Node.js, ElasticSearch, Postgres, cloud-enabled services, and other open-source tools.

Front-End Developer. The ideal candidate should have demonstrated ability in building and maintaining high-quality user interfaces that function properly in all supported browsers. Must be an experienced software developer with expert knowledge in Node.js, JavaScript, ES6, ReactJS, Sass, Bootstrap. Should have excellent understanding of usability and the ability to focus on precise details. Must be eager to learn about the modern front-end tools, standards and best practices. Read more about this position here

Full Stack Software Developer. The ideal candidate should have demonstrated ability in building complex web applications and should be proficient working in all stages of the software development cycle, from defining data models to deploying cloud services. Must be an experienced software developer with expert knowledge in Python, JavaScript, ElasticSearch, Postgres and Amazon Web Services. Should have solid understanding in working with JSON data and RESTful services, as well as writing unit tests and browser behavioral tests. Read more about this position here

Senior Biocuration Scientist (Scientific Data Curator 3): This individual will be responsible for working with other curation and software engineering staff in the specification, design, and implementation of tools that integrate, search, and display scientific and clinical data. Knowledge of variant detection, molecular mechanisms of disease, functional assays and computational analysis is needed in order to successfully define the use of these data. Familiarity with the use of controlled vocabularies and ontologies to describe biological concepts is a plus. Read more about this position here

For inquiries about any of the above positions, please contact Matt W. Wright at or Jimmy Zhen at

Academic Affairs Administrator - 80043

The Basic Science Administrative Consortium is a collective of 10 departments in Stanford University’s School of Medicine that share administrative support services. The Consortium is seeking an Academic Affairs Administrator who will be responsible for coordinating appointments, reappointments, and promotions for faculty, visiting scholars, and academic staff. The Academic Affairs Administrator will work under limited supervision and ensure compliance with school and university policies. 

Research Administrator 2 - 79658

The Department of Biomedical Data Science (DBDS) in the School of Medicine is seeking a Research Administrator 2. The successful candidate will be able to work in a dynamic environment, as part of a team, to manage the proposal preparation and post-award financial activities on grants, contracts, gifts, program projects, and federal grants, both routine and complex. We support approximately 25 PI’s and a consolidated budget of around $25M.

Postdoctoral Scholar (Newman Lab)

Faculty Mentor: Newman, Aaron

Department Name: Institute for Stem Cell Biology and Regenerative Medicine & Department of Biomedical Data Science

Postdoctoral Training Position Description:

The Newman Lab, in the Institute for Stem Cell Biology and Regenerative Medicine and the new Department of Biomedical Data Science at Stanford University, is seeking highly 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 advance our understanding of normal and neoplastic tissue composition at a molecular and cellular level (e.g., Nature Methods 2015, PMID 25822800). As part of this effort, we employ a variety of genomics approaches, including high throughput sequencing and emerging single cell profiling technologies. Successful applicants will be expected to leverage computational tools to address basic or clinical research questions in diverse areas of cancer/stem cell biology, including tumor differentiation and development, the cellular composition of the tumor microenvironment, and cell lineage relationships in malignant and normal tissues. Opportunities for wet lab biologists interested in data science will also be available. In addition, there will be ample opportunities to work closely with basic and clinical science collaborators, both at Stanford and elsewhere.

The successful applicant will have completed (or be close to completing) a Ph.D. or M.D./Ph.D. in an applied quantitative discipline, such as computational biology, bioinformatics, or biostatistics, with a strong interest in either basic or translational research. A strong background in machine learning and predictive modeling is desired, as is previous experience in common programming languages (e.g., R, Python) and genomic data analysis. Candidates with training in related fields, or in a life sciences discipline without formal computational training, will be considered depending on fit. Prior evidence of ambition, productivity, and creativity are a must, and a track record of conference presentations and first author peer-reviewed publications will be expected. Applicants should enjoy thinking deeply and working independently but also enjoy collaborating in a dynamic, fast-paced team environment.

Appointment Start Date: Until filled

Postdoc Appointment Term: 1 year, with the goal of extending the appointment to 3-5 years.

Required Application Materials: Recent CV, copies of relevant papers, names and contact information for 3 references, and a cover letter briefly describing the applicant’s previous research, scientific interests, and fit for this position.

How to Submit Application Materials: Please send to: and