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Open Postdoctoral position, faculty mentor Michael Gisondi

Important Info

Faculty Sponsor (Last, First Name): 
Gisondi, Michael
Stanford Departments and Centers: 
Emergency Medicine
Postdoc Appointment Term: 
January 16, 2020 (flexible); Term: 1 year with renewal for second/third year(s) contingent on performance and funding.
Appointment Start Date: 
January 2020
How to Submit Application Materials: 

To apply for this position, please send the required application materials to Dr. Stefanie Sebok-Syer (ssyer@stanford.edu). Please indicate in the subject: “PEARL Postdoc Application – YOURNAME”

The Precision Education and Assessment Lab (PEARL) is seeking a postdoctoral fellow to work in the Department of Emergency Medicine on a predictive learning analytics project sponsored by the American Medical Association (AMA). The fellow will be primarily mentored by Dr. Michael Gisondi and will work closely with Dr. Stefanie Sebok-Syer and Dr. Holly Caretta-Weyer.
 
AMA Project Synopsis:
The goal of this five-year project is to develop tiered entrustable professional activities (EPAs) and observable practice activities (OPAs) that measure specific learning outcomes of Emergency Medicine residents throughout their years of postgraduate training. These EPAs will integrate multiple knowledge and skill competencies and map to Accreditation Council for Graduate Medical Education (ACGME) Milestones in a way that supports a robust learner assessment ecosystem. Upon developing these EPAs and OPAs, we will create a digital dashboard to document and map residents’ learning trajectories. Finally, we will use machine learning and predictive learning analytic approaches to track the development of Emergency Medicine residents throughout postgraduate training and inform individualized education and assessment.
 
Primary Roles and Responsibilities:

  • Assist in the development and design of EPAs and OPAs to measure residents’ competencies.
  • Implement an ‘extract, transform, load (ETL)’system to extract data from EPA and OPA assessments.
  • Design a dashboard to surface insights into resident competencies and inform ongoing resident education and assessment.
  • Implement the dashboard and solicit feedback from users, and iterate for ongoing maintenance.
  • Deploy the ETL system and dashboard into a production environment.
  • Prepare technical reports and presentations documenting the development, deployment, and evolution of the dashboard and its underlying data (e.g., training material, best practices and lessons learned, and last-mile analyses).

The successful fellow will also have significant opportunity for professional development through structured mentorship activities.
 
Professional Development Activities:

  • Create a professional development plan with Dr. Sebok-Syer and conduct quarterly meetings to track overall progress towards the goals identified in the professional development plan.
  • Opportunities to improve communication and presentation skills (e.g., conference sessions, technical reports, and national/international presentations for various stakeholder audiences).
  • Opportunity to own an end-to-end analytics product used to inform resident education and assessment and publish peer-reviewed papers on the development, deployment, and evolution of the dashboard.
  • Opportunities to complete last mile analyses to open new research areas and establish a clear program of research.
  • Training, mentorship, and collaboration with investigators in The PEARL including active participation in weekly lab meetings.
Required Qualifications: 
  • PhD in Bioinformatics, Computer Science, Engineering, Biostatistics, Population Genetics, or a related area.
  • Experience in databases (e.g., SQL), ETL, and analytics (e.g., Python, R, SPSS, SAS) and visualization (e.g., Plot.ly, Shiny, Tableau, D3).
  • Proficiency in analytical methods (i.e., descriptive, exploratory, inferential, and predictive analytics).
  • Capability to own an end-to-end analytics product from inception to delivery.
  • Expertise in communicating with technical and non-technical users.
  • Strong interest in completing last-mile analyses by leveraging ETL, visualization, and/or analytics systems and driving change.
  • Ability to work independently, as well as part of a collaborative and interdisciplinary team.
Required Application Materials: 
  • Cover Letter
  • Curriculum Vitae
  • Contact information for 2-4 References

 

Stanford is an equal opportunity employer and all qualified applicants will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other characteristic protected by law.