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[FILLED] Applying statistical learning to personalize cardiovascular treatment

Starting September 2020

About us: We are a group of about twenty doctors, engineers, informatics professionals and students focused on enabling better care using existing health data. We develop novel methods to learn from patient-level health data including structured health encounter records, clinical notes, insurance claims, diagnostic imaging, and clinical trial data. A major research thrust is to answer clinical questions that enable better medical decisions using electronic health records (EHRs) and insurance claims data, via a consultation service that uses aggregate patient data at the point of care ( We also have an active research program to research safe, ethical, and cost-effective strategies for predictive models to guide mitigating care actions ( Our research group is part of the Department of Medicine at Stanford.

About the project: The research goal of this postdoctoral scholar position is to develop risk stratification tools and treatment effect estimation methods for cardiovascular disease (CVD). The successful candidate will develop personalized treatment effect prediction tools to guide decisions for CVD therapies based on their potential benefit and risk. The position offers the opportunity to work with leading Stanford faculty in Informatics (Nigam Shah), Statistics (Robert Tibshirani), Computer Science (Emma Brunskill), and Causal Inference (Stefan Wager).

About you: You are a hands-on team member who will collaborate with medical doctors, statisticians and computer scientists to develop methods for causal inference and personalized treatment effect prediction. You will contribute at all levels of the project: designing statistical analysis methods and experiments to evaluate them, implementing robust code, and releasing publicly available software packages.

You will find this project to be a good fit if you:

  • are passionate about improving health care using data science
  • know causal inference methods inside and out
  • are excited to work with rich, sometimes messy, patient-level data
  • thrive in dynamic, fast-paced environments

You look forward to responsibilities that include:

  • developing and evaluating novel statistical methods to derive actionable findings from healthcare data of millions of patients
  • programming in R and Python to produce scalable, reusable code
  • writing manuscripts and progress reports about your research
  • designing rapid prototypes and making some of them robust
  • working with a small core team of researchers
  • involvement in mentoring graduate students and teaching (as appropriate)

You meet all of the following requirements:

  • PhD in medical informatics, epidemiology, statistics or computer science
  • 2+ years of experience analyzing health data, such as insurance claims and EHRs
  • fluency in R
  • excellent written and oral communication in English, with at least one peer-reviewed first-author manuscript

You meet some of the desired qualifications:

  • experience in processing and analyzing large datasets
  • knowledge of best practices in data mining and machine learning
  • app and/or Web development

Interested? Send a cover letter, CV, and the contact information of 2+ references to acallaha [at] stanford [dot] edu

jobs/causal_inference_postdoc.txt · Last modified: 2022/12/12 10:55 by nigam