Harvard University Data Science Curriculum Fellow in Biomedical Informatics in Cambridge, Massachusetts


Title Data Science Curriculum Fellow in Biomedical Informatics

School Harvard Medical School

Department/Area DMBI

Position Description

Institution: Harvard Medical School Location: Boston, MA Category: Faculty – Science Type: Full Time

The Department of Biomedical Informatics (DBMI) at Harvard Medical School (http://dbmi.hms.harvard.edu) invites applications for a Data Science Curriculum Fellow, associated with the Master of Biomedical Informatics (MBI) programs (http://informaticstraining.hms.harvard.edu).

The MBI programs offered by DBMI provide an intellectual framework for the systematic and sound use of quantitative methods to increase agility with such methods in their respective domains. The programs include an intensive, hands-on boot camp, a range of foundational courses in quantitative and biomedical subjects, as well as courses in emerging areas such as precision medicine, data science, and data visualization. All students are expected to complete a capstone research project and to participate in a seminar series.

The MBI Curriculum Fellow will play a key role in developing the educational offerings of the Department of Biomedical Informatics and will:

• Develop or identify Biomedical Informatics-related content and materials for courses and serve as the primary instructor or provide instructional support to the associated teaching faculty. • Take a lead role in planning and mentoring the capstone research projects. • Work with the MBI Director and faculty to expand the MBI curriculum and keep it current. • Mentor teaching assistants. • Pursue additional opportunities to improve Biomedical Informatics education through collaborations and the adoption of novel course formats. • Liaise with other Harvard departments and affiliates engaged in data science teaching and learning initiatives. • Contribute to program delivery and coordinate appropriate follow-up to drive continuous course and program improvement. • Develop, implement, and support paracurricular activities, including symposia, recruitment events, social and career development initiatives. • Collaborate with the program manager to foster a positive learning environment for MBI students and be a continuous presence with the student cohort. • Provide administrative support, including the preparation of annual reports for university stakeholders together with the program manager. • Participate in strategic planning initiatives and program development.

We aim to recruit a postdoctoral scientist who is committed to both the practice and the teaching of Biomedical Informatics and who is drawn to an academic environment. The MBI Curriculum Fellow is a member of the HMS Curriculum Fellows Program, a science education community that functions as a training program for early career scientist-teachers while contributing to the development and oversight of high quality, innovative graduate education at Harvard. Pedagogical training is incorporated into the fellow’s course- and program-based efforts and includes opportunities to design and conduct discipline based educational research (DBER) projects. Curriculum Fellows receive mentorship and career advising to support their development as educators throughout the duration of the appointment.

Basic Qualifications

• An earned doctorate in a Biomedical Informatics-related field (i.e. computational biology, computer science, etc) • Teaching experience at the graduate or undergraduate level • Postdoctoral research or teaching experience (preferred)

Additional Qualifications

• Demonstrated quantitative expertise and experience with biomedical data • Proficiency with R and/or Python (preferred) • Familiarity with practices related to collaborative and reproducible scientific work • Ability to work collaboratively with multiple faculty and administrative partners to develop new courses and educational/training opportunities • Experience applying pedagogical best practices, including evidence-based teaching methods and technological innovations, to the classroom • Ability to advise faculty on the application of evidence-based teaching practices to existing courses • Strong oral and written communication skills • Strong organizational and administrative skills and the ability to successfully plan and implement programs and events • Ability to work independently to identify and implement optimal solutions to diverse problems • Effective mentorship skills

Special Instructions

To be considered for this position, please email a single pdf document containing a letter of application that addresses your interest in and qualifications for the position, a curriculum vitae, and a statement of teaching philosophy. Please include the names and contact information of three references, who will be asked to supply letters or will be contacted by phone early in the application screening process. You may also include evidence of teaching effectiveness or links to relevant work. Applications will be considered on a rolling basis, with a deadline for full consideration of 5 PM Eastern Time on July 7th, 2017. The CF position and its associated academic appointment is a one year appointment, renewable for three years, and is non-tenure-track. Fellows are hired at the rank of research associate or Lecturer, commensurate with experience. The ideal start for the position is on or before August 1, 2017 at Harvard Medical School in Boston.

Address applications to Bradley Coleman, Ph.D. Co-Director, Curriculum Fellows Program Via Ms. Naima Abdullahi (cfp@hms.harvard.edu)

Contact Information

Address applications to Bradley Coleman, Ph.D. Co-Director, Curriculum Fellows Program Via Ms. Naima Abdullahi (cfp@hms.harvard.edu)

Contact Email cfp@hms.harvard.edu

Equal Opportunity Employer

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

Minimum Number of References Required

Maximum Number of References Allowed

Supplemental Questions