Research Assistant Professor – Electronic Health Record Data Analysis and Phenotyping
The Department of Pathology and Laboratory Medicine at the Perelman School of Medicine at the University of Pennsylvania seeks candidates for an Assistant Professor position in the non-tenure research track. Expertise is required in the specific area of electronic health record data collection, cleaning, analysis, and use in research activities. Applicants must have a Ph.D. degree.
Research or scholarship responsibilities may include collecting, organizing, analyzing, and interpreting results derived from electronic health record data using a variety of statistical methods. The successful applicant will work as part of an interdisciplinary team investigating ways to interrogate multimodal patient data (radiology images, histopathology images, EHR data) to generate more accurate diagnostic models, identify patient cohorts, and generate new diagnostic tools.
Applicants must have demonstrated excellent qualifications in research. A minimum of 5 years of relevant experience in an academic research environment is required. Leadership qualities and strong communications and interpersonal skills are also necessary.
Experience in developing and implementing electronic phenotype algorithms and/or natural language processing is highly desirable. Significant experience in a variety of statistical methods and 5 years of research experience with EHR/data analysis strongly preferred. In addition, experience in combining EHR data with other types of data (genomic, environmental, social media, wearables, etc.) is also highly desirable. The successful applicant should be comfortable with writing algorithms, programs, and analysis pipelines. Strong presentation skills (written and oral) are important.
Apply online at: apply.interfolio.com/71902
We seek candidates who embrace and reflect diversity in the broadest sense. The University of Pennsylvania is an EOE. Minorities/ women/ individuals with disabilities/ protected veterans are encouraged to apply.