Careers

Job Description

The Center for Computational Biomedicine (CCB) seeks an engaged computational scientist to join our team.  The candidate will have strong statistical skills, good working knowledge of scientific programming and a strong interest in applying complex algorithms to epidemiological and omics data sets.   The CCB has a number of active research projects in epidemiology and biomedical informatics as well as projects in spatial omics.  Candidates should have skills in developing and implementing complex analysis methods to large data sets.

CCB is building an ecosystem of very large genomic and health services datasets, including a wide variety of covariate data sets including environmental exposures and social determinants.  Combined, these data sets provide a powerful platform for studying health outcomes, epidemiology, health care policy and economics, health disparities, among many other research areas.  These resources, along with the computational tools required to analyze them, will be available to enable the next generation of AI research applied to these areas.  The Center has access to a cluster containing, in aggregate, over 11,000 compute cores, 90 TB of RAM, over 100 NVIDIA accelerator cards, and 10 PB of all-flash storage.  In addition to the compute cluster, CCB maintains a number of large database servers each with 72 CPU cores, 3 TB of RAM, and 64 Gbps aggregate storage IO.

Work Group

  • Computational Biology

Requirements

  • Ph.D or equivalent in Statistics, Biostatistics, Computer Science or related field.
  • Ability to develop complex and high performance algorithms and software infrastructures
  • Experience and understanding of AI/ML algorithms and practices
  • Good communication skills
  • Ability to work on teams

Additional Qualifications

  • Post-doc or work experience using advanced computational and analytic skills
  • Understanding of high throughput biology (e.g. transcriptomics, imaging, flow cytometry, proteomics)

Send letter of interest and CV toLudwig_Geistlinger@hms.harvard.edu

Job 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.

Work Group

  • Education

Job Duties

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:

Requirements

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

Additional Qualifications

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:

Applications received by November 1, 2023 will receive a full review. Review of applications will begin on November 2, 2023. Applicants who apply after the deadline must email cfp@hms.harvard.edu to alert the hiring team you’ve applied.

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, herehttps://curriculumfellows.hms.harvard.edu/teaching-statement-guidelines
  • The names and contact information of three professional references.