Details
| Title | Assistant/Associate 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 or Associate Professor level in the field of statistics or machine learning, with demonstrated interest in single-cell or spatial 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 or spatial technologies as part of their research, as well as with faculty across Harvard and its 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 or spatial 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 an 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 will be reviewed on a rolling basis but must be submitted by 10/31/2026. Four references and two publications will be requested of candidates applying at the Assistant Professor level. Six references and five publications will be requested of candidates applying at the Associate Professor level. For guidance on the Service Statement, one of the required application documents, click to learn more about the Harvard Chan School Principles of Citizenship . The following topic areas may help frame and organize statements but are not meant as a template as there is no strict checklist of requirements. Rather, statements should directly reflect the specific past efforts and sincere future plans of each candidate:
|
| Contact Information | Shanyce Hanson |
| Contact Email | [email protected] |
| Salary Range | Assistant Professor rank: $154k-$180k Associate Professor rank: $200k-$249k |
| Minimum Number of References Required | 4 |
| Maximum Number of References Allowed | 6 |
| Keywords | biostatistics; machine learning; statistics; computational biology; genomics |
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