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Harvard University Assistant Professor of Biostatistics and Data Science in Cambridge, Massachusetts

Details

Title Assistant Professor of Biostatistics and Data Science

School Harvard T.H. Chan School of Public Health

Department/Area Biostatistics/Data Science

Position Description

The Departments of Biostatistics at the Harvard T.H. Chan School of Public Health and Data Science at the Dana-Farber Cancer Institute provide exceptional environments to pursue research and education in quantitative methods, while leading global efforts to improve the health of individuals and populations. Faculty members work closely together across the two departments, and joint appointments are common. Our faculty are experts in a wide range of statistical and computational biology areas, and applied research is facilitated through collaborations with researchers in public health, medicine, and related fields. Ample opportunities exist for collaboration with biomedical researchers at Harvard-affiliated hospitals, within the Harvard schools, The Broad Institute, and MIT .

The Department of Data Science at the Dana-Farber Cancer Institute ( DFCI ) and the Department of Biostatistics at the Harvard T.H. Chan School of Public Health ( HSPH ) seek candidates to fill a tenure-track faculty position at the Assistant Professor level in the field of statistics or machine learning, with demonstrated interest in single-cell genomics applications. This faculty member will have a unique opportunity to collaborate with basic scientists and biomedical researchers at DFCI , particularly those generating data using single-cell technologies as part of their research, as well as with faculty across Harvard and it affiliates. Because the position carries an appointment at HSPH , they will be expected to participate fully in departmental and school activities, including teaching graduate-level courses and mentoring students and postdoctoral fellows.

The faculty member will hold a primary appointment in the Department of Data Science at DFCI , and a faculty appointment in the Department of Biostatistics at HSPH . Resources will be provided to support the development of a research team, including postdoctoral fellows and graduate students.

Basic Qualifications

Qualified applicants will have a doctoral degree in biostatistics, statistics, computer science, computational biology, or a related field. Candidates must have completed their doctoral degree prior to the start date of the position.

Additional Qualifications

The candidate should exhibit strong interest in single-cell genomics applications, and should be enthusiastic about teaching and training through graduate programs. The candidate should also possess the ability to work collaboratively with other scientists within the Biostatistics Department and should espouse the scholarly qualities required to teach and mentor doctoral students. Candidates should be committed to building a safe and inclusive institutional culture that respects and builds upon our many differences. These principles of citizenship are respect, integrity, collegiality, and commitment to a more supportive and sustainable world.

Special Instructions

Applications must be received by February 1, 2025. Four references and two publications will be requested of candidates.

For guidance on the Service Statement, click to learn more about the Harvard Chan School Principles of Citizenship (https://www.hsph.harvard.edu/about/) .

Contact Information

Shanise Belizaire

Contact Email chair@ds.dfci.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, 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 4

Maximum Number of References Allowed 6

Keywords

biostatistics; machine learning; statistics; computational biology; deep learning

Supplemental Questions

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