Skip to content Skip to navigation

Open Postdoctoral position, faculty mentor Elsie Ross

Important Info

Faculty Sponsor (Last, First Name): 
Ross, Elsie
Department Name: 
BMIR
Postdoc Appointment Term: 
8/01/2019-7/31/2020
Appointment Start Date: 
Flexible
How to Submit Application Materials: 

Postdoctoral Position in Machine Learning and Precision Health for Cardiovascular Diseases at Stanford University School of Medicine
Join an exciting translational data science and technology lab at the world class Stanford University School of Medicine!
The Ross Lab in the Division of Biomedical Informatics Research and Division of Vascular Surgery at Stanford University School of Medicine is looking to recruit an outstanding postdoctoral scholar with an interest in building precision health care tools to enable next generation health care delivery. This position provides the opportunity to perform a deep dive into the clinical area of cardiovascular diseases and utilize large scale data from electronic health records and biobanks in order to build real-time personalized tools for patient risk stratification and make personalized treatment recommendations.
 
Atherosclerotic cardiovascular disease is one of the leading killers of adults in the Western world and leads to billions of dollars in annual health care expenditures. The Ross lab has a special focus on an advanced form of cardiovascular disease known as peripheral artery disease, and uses big data and advanced analytical tools to develop important clinical insights and translate these insights into actual patient care.
 
The Ross lab is located on the Stanford University School of Medicine main campus, which enables multidisciplinary collaborations with teams and world leaders in the fields of biomedical informatics, computer science, epidemiology and clinical research. Opportunities for scientific and career growth are abundant.
 
Potential projects include (but are not limited to):
• Developing electronic health record based risk classification and prediction models for vascular disease
• Evaluating the role of feature engineering in classification of chronic diseases
• Comparing performance of disease classification models in different health care settings
• Building a precision health pipeline to predict treatment response and provide recommendations
This position is funded internally for a year with the possibility of an additional year of funding.

Required Qualifications: 
  1. A PhD in computer science, computational biology or biomedical informatics with experience in the application of machine learning to diverse problems.
  2. Strong coding skills in R, Python, Unix are expected. A working knowledge of deep learning algorithms is preferred.
  3. Evidence of written scholarship and excellent written scientific English skills
  4. Enthusiasm for learning new skills
  5. Experience with building production ready data science applications is a plus, but not required
Required Application Materials: 
  1. A complete CV with a list of publications
  2. A cover letter describing your research interests and qualifications
  3. Contact information of 3 references

 

Stanford is an equal opportunity employer and all qualified applicants will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other characteristic protected by law.