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Clinical Machine Learning

National Library of Medicine, Bethesda, MD and surrounding area

Position Description:

The National Library of Medicine has an opening for a postdoctoral fellow in the lab of Dr. Jeremy Weiss to develop and evaluate machine learning methods for clinical informatics problems. Research directions may include but are not limited to: increasing performance and trustworthiness of deep longitudinal methods, solving challenges posed by electronic health records, and translating clinical needs into algorithmic solutions.

Of particular interest is a fellow focusing on clinical machine learning. We are seeking to develop methods that learn from longitudinal, multi-modal data present in electronic health records data that (1) are predictive and useful for forecasting, and (2) that preserve quantitative properties throughout training and computation. Relevant experience includes expertise in python and representation learning, geometric deep learning, signal processing, longitudinal data, and multi-modal data.
 
Please review the following for additional information:
 

Qualifications:

  • PhD, MD, or equivalent in related subject areas, including machine learning, computer science, medicine, bioinformatics, and statistics/biostatistics,
  • Excellent programming skills in Python, R, Julia, or C++;
  • Publishing experience with peer-reviewed journals and/or conferences in the above areas.
  • Appointees may be U.S. citizens, permanent residents, or foreign nationals (visa requirements apply). 
Timing: the position is for 1 year with the possibility for extension. Candidates are subject to a background investigation.  
 

To Apply:

Applicants should email the materials below to:
 
Jeremy Weiss, MD, PhD
Care Health and Reasoning Machines (CHARM) Lab
National Library of Medicine (NLM)
c/o Virginia Meyer, PhD
  • Cover letter with a short research statement and preferred starting date
  • CV
  • Link(s) to published artifacts (packages, github repos, etc)
  • Contact information for 3 references.
Application Deadline Date: Applications will be accepted until the position is filled.
 
 
The NIH is dedicated to building a diverse community in its training and employment programs and encourages the application and nomination of qualified women, minorities, and individuals with disabilitiesDHHS and NIH are Equal Opportunity Employers.