Please email Marlena Christina Sandoval at email@example.com with required application materials.
The Most clinical research team in the Division of Facial Plastic & Reconstructive Surgery within the Department of Otolaryngology-Head & Neck Surgery is seeking a highly motivated postdoctoral scholar to join our team. Our group studies outcomes in facial plastic surgery procedures through prospective, retrospective and large-data-set studies. Our overarching goal is to improve the evidence base in facial plastic surgery in order to improve patient management.
The postdoctoral scholar will work on several projects/studies related to outcomes of procedures such as functional surgery of the nose, nasal reconstruction, and aesthetic surgery. Particular areas of interest in our group are prediction of outcomes independent of technical outcome. We have developed a patient-reported outcome measure (PROM) that has been the basis for much of our outcomes work. Prior studies from our group have used PROMs to demonstrate that some outcomes may be predicted based on patient psychology. In addition, we are developing model(s) for use of machine learning (using patient images) to predict outcomes. We are particularly interested in applications of machine learning in this regard.
We are looking for a motivated, creative, and qualified scientist with excellent verbal and written communication skills. Women and under-represented minorities in academia are strongly encouraged to apply.
• PhD and/or MD degree in life sciences, medicine, or related fields conferred prior to start date
• Prior experience working in clinical and/or psychology research
Preferred skills include any of the following:
• Well-versed in concepts of statistical analysis with prior data science programming expertise
• Prior experience working with commercial databases like IBM marketscan, Komodo or similar
• A brief cover letter highlighting prior experience and reasons for interest in the position
• Names and contact information for 2-3 references
• Copies of key prior paper(s) led by the applicant