Curriculum Fellow in Artificial Intelligence/Machine Learning

Harvard University, United States

Updated: about 2 months ago
Location: Cambridge, MASSACHUSETTS
Deadline: ;

Details


Title Curriculum Fellow in Artificial Intelligence/Machine Learning
School Harvard Medical School
Department/Area Center for Computational Biomedicine (CCB)
Position Description
The Harvard Medical School Curriculum Fellows Program (HMS CFP) welcomes applications for an Artificial Intelligence/Machine Learning (AI/ML) Curriculum Fellow (CF) for the Center for Computational Biomedicine (CCB). The CFP is a postdoctoral service and training program intended for early-career scientist-educators, focused on curriculum development, teaching, and educational programming in the biological and biomedical sciences.
The CCB AI/ML Curriculum Fellow is part of a larger cohort of Curriculum Fellows, and will work closely with members of the Harvard Medical School faculty and administration to develop, deploy, and evaluate evidence-based graduate training. Fellows also receive mentorship and career advising to support their development as educators and help them succeed in a variety of education-focused careers. CFs are appointed as Research Fellow and have the opportunity to apply for promotion to Lecturer during their appointment. CFs are also encouraged to pursue additional activities that align with their professional goals, such as publishing research, participating in academic conferences, or teaching at local universities. More details can be found on our website (https://curriculumfellows.hms.harvard.edu/ ).
The CCB develops shared data and analytic resources that broadly serve HMS. This includes a strong educational mandate for courses and skills related to software, data analysis, technical computational skills and the adoption of new methods (eg AI/ML, single cell RNA-seq, etc). The AI/ML CCB CF will be responsible for developing curricula for AI/ML-related CCB workshops offered to HMS graduate students, postdocs, research staff and faculty across HMS programs and departments. The CF will report directly to CCB’s Director of Education and will receive one-on-one mentorship from the CCB Executive Director, Dr. Robert Gentleman and the Director of the CFP.
AI and ML skills are invaluable in biomedical science because they enable researchers to process and make sense of complex data, accelerate research processes, improve disease diagnosis and treatment, and contribute to the development of more personalized and effective healthcare solutions. Researchers and professionals with expertise in AI and ML can make significant contributions to the advancement of biomedical science and the improvement of healthcare outcomes. CCB is seeking a CF who will focus on developing, curating, and delivering AI/ML skills training to HMS students, postdocs, research staff and faculty to ensure HMS researchers remain at the forefront of innovation in their field of study.
The primary responsibilities of the CCB AI/ML CF are expected to include:

  • Working with the CCB’s Director of Education to implement CCB educational initiatives
  • Serving as the liaison between the CCB and other HMS departments to identify educational gaps in AI/ML skills training and develop a strategic plan for addressing them via on-line courses, self-study resources, classes, and workshops
  • Liaising with existing groups engaged in integrating AI/ML training into their programs at HMS to identify available resources and coordinate training, curriculum development, and outreach efforts
  • Creating curricula for and teaching CCB-hosted workshops, on-line courses, asynchronous resources, and in-person courses for HMS students, postdocs, research staff, and faculty
  • Using and developing curricula using AI/ML models and approaches for CCB-hosted workshops and other training offerings
  • Lead and intellectually contribute to one of CCB’s scholarship of teaching and learning projects with the expectation that findings collected during the fellowship will be presented at conferences and/or published in a scientific/education journal


Additionally, the CCB AI/ML Curriculum Fellow will also have specific responsibilities to the CFP:

  • Take required courses including Teaching 100 and Teaching 101
  • Participate in weekly CFP group meetings and pedagogical journal clubs.
  • Present at university-wide workshops on curricular and pedagogical topics.
  • Assist in developing the curriculum for the NIH-mandated Responsible Conduct of Science (RCOS) course.
  • Assist with the organization of the Graduate Science Education Series (GSES), https://curriculumfellows.hms.harvard.edu/graduate-science-education-series

Basic Qualifications:
Candidates are expected to have a PhD or equivalent degree in Computational Science, Computational Biology, or a related field, along with experience applying AI/ML approaches to biological problems. Candidates who are currently finishing their doctoral work but have not yet graduated are encouraged to apply.
Qualified candidates will be evaluated based upon their:
  • Demonstrated interest or experience with teaching and/or curriculum development in higher education settings
  • Comfort and experience teaching and developing curriculum in an asynchronous online environment
  • Ability to bridge the gaps between computational and biomedical sciences
  • Organizational and written and oral communication skills
  • Ability to support collaborations across departments in a fast-paced academic environment



The ideal start date for this Curriculum Fellow is approximately April 1st, 2024. This is a hybrid position and the candidate will be expected to work in person on the HMS campus in Boston, MA 2-3 days per week. The CF appointment is renewable annually for a maximum of three years and is non-tenure-track.

To apply:
Position open until filled. Please send us an email to let us know you’ve applied so that we can review your application. Please email [email protected] .
Please submit the following materials:
  • A cover letter that addresses your interest in and qualifications for the position. Please highlight your experience in AI/ML approaches to computational science, bioinformatics or a related field in your cover letter.
  • A curriculum vitae.
  • A teaching statement. The teaching statement is an opportunity to describe your philosophy of teaching in the context of your own experiences. A discussion of diversity, equity and inclusion is an important component of the teaching statement. Submissions will be evaluated according to the guidelines found on our website, here , https://curriculumfellows.hms.harvard.edu/teaching-statement-guidelines
  • The names and contact information of three professional references.
Basic Qualifications
Candidates are expected to have a PhD or equivalent degree in Computational Science, Computational Biology, or a related field, along with experience applying AI/ML approaches to biological problems. Candidates who are currently finishing their doctoral work but have not yet graduated are encouraged to apply.
Additional Qualifications
Special Instructions
Contact Information
Bethany Krevat
Curriculum Fellows Program Coordinator
Contact Email [email protected]
Equal Opportunity Employer
We are committed to cultivating an inclusive workplace culture (https://hr.fas.harvard.edu/inclusive-culture ) of faculty, staff, and students with diverse backgrounds, styles, abilities, and motivations. We appreciate and leverage the capabilities, insights, and ideas of all individuals.
Harvard Medical School Mission and Community Values: https://hms.harvard.edu/about-hms/campus-culture/mission-community-values-diversity-statement
We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, gender identity, sexual orientation, pregnancy and pregnancy-related conditions or any other characteristic protected by law.
Minimum Number of References Required 3
Maximum Number of References Allowed 3
Keywords
artificial intelligence, machine learning, data, data science, computational biology, biomedicine, biology, biomedical science, data, science, curriculum, teaching, faculty, postdoc, genetics, neurology, neurobiology, neuroscience

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