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Department of Epidemiology and Biostatistics Faculty Position – Epidemiology Assistant or Associate Professor

LinkedIn University of California, San Francisco San Francisco, CA
Not Applicable Posted April 3, 2026 Job link
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Requirements
  • graduate-level training in epidemiology, public health, informatics or a closely related field;
  • expertise, with a publication record, in the methods of contemporary predictive analytics as applied to clinical decision-making and healthcare delivery; we define this as including development and assessment of diagnostic tests, multi-feature prediction rules (including those derived with machine learning), and how test results and prediction rules can guide clinical decisions (i.e., test utility);
  • strong interest and experience in teaching the methods of predictive analytics in both formal and informal settings, including to audiences without extensive mathematical backgrounds;
  • record of accomplishment in applied research related to predictive analytics; for Associate-level applicants, this includes a history of extramural funding;
  • experience with electronic medical record-based informatics and/or research; and
  • familiarity with the methods of modern causal inference.
  • Individuals with research interests in any subject matter domain are welcome to apply.
  • Curriculum Vitae - CV must clearly list current and/or pending qualifications (e.g. board eligibility/certification, medical licensure, etc.).
Preferred Skills
  • graduate-level training in epidemiology, public health, informatics or a closely related field;
  • strong interest and experience in teaching the methods of predictive analytics in both formal and informal settings, including to audiences without extensive mathematical backgrounds;
  • record of accomplishment in applied research related to predictive analytics; for Associate-level applicants, this includes a history of extramural funding;
  • experience with electronic medical record-based informatics and/or research; and
  • Individuals with research interests in any subject matter domain are welcome to apply.
Education
  • (Not required) – a doctorate-level professional degree in medicine, pharmacy, or nursing (or equivalent clinical experience);
  • (Not required) – graduate-level training in epidemiology, public health, informatics or a closely related field;
  • (Not required) – expertise, with a publication record, in the methods of contemporary predictive analytics as applied to clinical decision-making and healthcare delivery; we define this as including development and assessment of diagnostic tests, multi-feature prediction rules (including those derived with machine learning), and how test results and prediction rules can guide clinical decisions (i.e., test utility);
  • (Not required) – record of accomplishment in applied research related to predictive analytics; for Associate-level applicants, this includes a history of extramural funding;